On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks

On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks: NP-Completeness and Approximation Algorithms

ABSTRACT:

In many applications, it is a basic operation for the sink to periodically collect reports from all sensors. Since the data gathering process usually proceeds for many rounds, it is important to collect these data efficiently, that is, to reduce the energy cost of data transmission. Under such applications, a tree is usually adopted as the routing structure to save the computation costs for maintaining the routing tables of sensors. In this paper, we work on the problem of constructing a data aggregation tree that minimizes the total energy cost of data transmission in a wireless sensor network. In addition, we also address such a problem in the wireless sensor network where relay nodes exist and consider the cases where the link quality is not perfect. We show that these problems are NP-complete and proposeOð1Þ-approximation algorithms for each of them. Simulations show that the proposed algorithms have good performance in terms of energy cost.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

In many data aggregation algorithms, a tree is used as the routing structure, especially for applications that have to monitor events continuously. The reason for this is that sensors, which usually have limited resources, can save relatively high computational costs for maintaining routing tables if the sensors route packets based on a tree. While several papers target the maximization of the network lifetime. The problem of minimizing the total energy cost is also well studied in the literature. Moreover, for some indoor applications, the sensors may have AC power plugs. For example, the sensor of the Octopus wireless sensor network. Under such circum-stances or energy conservation activity, energy saving then becomes the major issue.

DISADVANTAGES OF EXISTING SYSTEM:

  • Problem of constructing a data aggregation tree with minimum energy cost.
  • Decrease Network Lifetime

PROPOSED SYSTEM:

  • We prove the problem of constructing a data aggregation tree with minimum energy cost, termed MECAT, is NP-complete and provide a 2-approximation algorithm.
  • We study the variant of such a problem in which the relay nodes exist, termed MECATRN.
  • We show that the MECAT RN problem is NP-complete and demonstrate a 7-approximation algorithm.
  • We show that any-approximation algorithm of the capacitated network design (CND) problem can be used to obtain a2-approximation algorithm of the MECATRN problem.
  • We extend the MECAT problem and the MECATRN problem to cope with cases where the link quality is not perfect and proposeOð1Þ-approxi-mation algorithms.
  • We conduct several simulations to evaluate the performance of the proposed algorithms.

ADVANTAGES OF PROPOSED SYSTEM:

  • Constructing a data aggregation tree with minimum energy cost.
  • Increase Network Lifetime.

MODULES:

  • The Network Model
  • Approximation Algorithm
  • Data Aggregation with Relay Nodes
  • Coping With Imperfect Link Quality
  • Performance Evaluation

MODULES DESCRIPTION:

The Network Model

We model a network as an undirected connected graph with weights and associated with each node, respectively, where V is the set of nodes, E is the set of edges, and r is the sink. Each node v has to send a report of sizes to sink r periodically in a multi-hop fashion along the unique path on a routing tree. A routing tree constructed for a network with sink r is a tree with root r. Let Tx and Rx be the energy needed to send and receive a packet, respectively. While routing, a hop-by-hop aggregation is performed according to the aggregation ratio, q, which is the size of the reports that can be aggregated into one packet. Because it would be meaningless if the aggregation ratio is set to a non-integer, the aggregation ratio is assumed to be an integer throughout this paper. It is note-worthy that we implicitly assume that the transmission energy and the receiving energy of a packet are constants.

Approximation Algorithm

As the MECAT problem is NP-complete, we provide an approximation algorithm. Observe that while sending a packet to the sink, the longer the routing path is, the greater the energy cost is. Naturally, we would route each packet via a shortest path to the sink. The resulting routing structure is then a shortest path tree. There are at least three benefits to route packets using a shortest path tree. First, a shortest path tree is easy to construct in a distributed manner, as described in the following two steps. The sink node first broadcasts a message such that each node can evaluate the hop distance from the sink. Then, each node sets its parent to the node with a smaller hop distance from the sink. Second, in many time-critical applications, it is necessary to route packets using a shortest path tree to achieve the minimum packet transmission delay. Third, we can construct the shortest path tree even in the case where the aggregation ratio or report sizes are not known or vary from time to time. Although a shortest path tree may not have the minimum energy cost (see Fig. 1 for an example), Theorem 2 shows that a shortest path tree algorithm has an approximation ratio of 2. That is, regardless of the values of the aggregation ratio and the report size, the energy cost of a shortest path tree is no greater than two times the energy cost of the optimum solution.

Data Aggregation with Relay Nodes

To improve the network connectivity or survivability, the relay node placement problem in a wireless sensor network has been extensively investigated in the literature .These relay nodes, which do not produce reports, are used to forward the packets received from other nodes. In this section, we study the problem of constructing a data aggregation tree with minimum energy cost in the presence of relay nodes.

Coping With Imperfect Link Quality

we have implicitly assumed that the link quality is perfect. That is, packet transmission always succeeds. In this section, we first define a new objective function, that is, a new way to calculate energy cost, for the MECAT problem and the MECATRN problem to take the effect of imperfect link quality into consideration. We then propose approximation algorithms for the modified problems.

Performance Evaluation

We study the problem of constructing energy-efficient data aggregation trees.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Tung-Wei Kuo, Kate Ching-Ju Lin, Member, IEEE, and Ming-Jer Tsai, Member, IEEE, “On the Construction of Data Aggregation Tree with Minimum Energy Cost in Wireless Sensor Networks: NP-Completeness and Approximation Algorithm”,  IEEE TRANSACTIONS ON COMPUTERS, VOL. 65, NO. 10, OCTOBER 2016

 

ESync: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks

ESync: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks

ABSTRACT:

Recent years have witnessed many new promising technologies to power wireless sensor networks, which motivate some fundamental topics to be revisited. Different from energy harvesting, which generates dynamic energy supply, the mobile charger is able to provide a stable and reliable energy supply for sensor nodes and, thus, enables sustainable system operations. While previous mobile charging protocols focus on either the charger travel distance or the charging delay of sensor nodes, in this work, we propose a novel energy synchronized mobile charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of traveling salesman problem (TSP)-based solutions, when the nodes’ energy consumption is diverse, we construct a set of nested TSP tours based on their energy consumption, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging request sequence of nodes with their sequence on the TSP tours. Experimentation and simulation demonstrate that ESync can reduce charger travel distance and nodes’ charging delay by about 30% and 40%, respectively.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

The Existing most intuitive approach is to periodically charge nodes along an optimal TSP tour constructed based on network deployment. The problems of mobile energy replenishment and data gathering are jointly optimized. Several other designs tackle the mobile charging problem from the perspective of individual sensor nodes. A scheme jointly exploring the routing and charging of individual nodes is proposed, which proactively guides the routing activities in the network and delivers energy to where it is needed. A greedy charging algorithm that always charges the node with the shortest remaining lifetime to its full capacity is proposed and is further improved by incorporating the remaining energy levels of other nodes when determining which node to charge next and how much energy to charge to. The design is validated based on a proof-of-concept prototype of a wireless charging system for sensor networks. Another way to greedily perform the charging tasks is to always select the nearest requesting node to charge, i.e., the Nearest-Job-Next discipline. The performance of Nearest-Job-Next is analytically evaluated. Although asymptotically promising, the worst-case performance of Nearest-Job-Next is difficult to guarantee.

DISADVANTAGES OF EXISTING SYSTEM:

  • More travel distance of the charger.
  • Increase charging delay of the sensor node.

PROPOSED SYSTEM:

Our work is the first to jointly improve the charging process for both sensor nodes and the mobile charger. Most existing works choose only one of the two given aspects as the design objective. At the macro level of the mobile charging process, to leverage the advantage of the TSP-based solutions while minimizing the impact of their limitations when node energy consumption is highly diverse, we construct a set of nested TSP tours based on the energy consumption rates of sensor nodes. Then, for each round of the charging process, a novel tour selection algorithm is designed to only involve the energy-hungry nodes into the charging schedule during that round. At the micro level, focusing on the charging schedule during individual rounds, observing that the nodes’ charging request sequence may significantly affect the charging performance, we propose the concept of energy synchronization among nodes to proactively match the nodes’ charging request sequence to the selected TSP tour in each charging round, which is achieved by care-fully selecting the node to be charged next and control-ling the amount of energy charged to individual nodes.

As a result, both the charger travel distance and the charging delay of sensor nodes are reduced. We evaluate the performance of ESync through both experiment and simulations, and the results demonstrate that ESync can reduce the charger travel distance and charging latency by 30% and 40%, respectively.

ADVANTAGES OF PROPOSED SYSTEM:

  • Our design reduces the charger travel distance by scheduling based on the nested TSP tours and reduces the charging delay of sensor nodes with the concept of energy synchronization within each round of selected tours.

MODULES:

  • Simulation Setup
  • Energy Synchronization Among Nodes
  • Performance Evaluation

MODULES DESCSRIPTION:

Simulation Setup:

We simulate an environment monitoring sensor network with 9 randomly deployed nodes. The sensing field size range from 3000 * 3000 m, and a mobile sink (charger) is located at the field center. The nodes’ communication range is 20 m to form a multihop network topology. The nodes’ energy capacity is 100 mAH. The energy consumption rate for the sensing tasks is 0.75 mA×2V=1.5 mW, which is typical for a light sensor. The communication energy costs of sensor nodes are set

based on the data of node: With transmitting and receiving current draw of 25 and 8 mA, respectively, the corresponding energy consumption rates are 25 mA× 2V=50mW and8mA×2V=16 mW with a typical voltage of 2 V. After node deployment, a routing structure is constructed based on the TinyOS standard CTP. Then, the environment information, after being captured by individual nodes, is transmitted to the sink through multihop communications. Sensor nodes send out charging requests to the charger when their remaining energy approaches zero. In our event-driven simulator, the sensory data are simulated as events occurred at random time and random locations—whenever an event happens in the sensing range of sensor nodes, these nodes would capture the event in different details and transmit the data to the sink via the constructed routing structure. This way, we do not take the data rates of sensors as control parameters in our simulation, but generate them according to the event occurrence rates. It is true that under such a scenario, the nodes near the sink would have higher energy consumption rates, which can be inferred, showing the routing structure of a particular node deployment in our simulations. The charger travel speed is 1 m/s, unless otherwise specified. We simulate a network operation period of 45 s and record the total distance that the mobile charger traveled and the total charging delay of sensor nodes. We adopt Concorde, which is an open-source TSP solver with verified efficiency, to obtain the near-optimal TSP tours in our simulation. The mobile charging process is simulated with NS2.

Energy Synchronization among Nodes: 

With the nested TSP tours and the tour selection method, only energy-hungry nodes are involved in each charging round. Here, we further improve the charging process by synchronizing the energy supply of nodes to proactively adjust their charging request sequence. As a result, the charging request sequence of nodes is synchronized with their sequence on the TSP tours. Specifically, if the charging requests from two neighboring nodes are sent out according to their sequence on the selected TSP tour, we say that these two nodes are energy synchronized in the charging process. This energy synchronization among nodes is achieved by carefully controlling the amount of energy charged to individual nodes. In general, the mobile charger needs to address two questions to carry out the charging process in each round: Which node should be charged next, and how much energy should be charged to the node.

Which Node to Charge Next:

In our design, the node to be charged next is determined according to the selected TSP tour in each round. Specifically, after completing the charging of the current node, the mobile charger selects the requesting node that is closest to its current location along the TSP tour as the next node to charge. Although, our method demonstrates a greedy feature, which may cause the unfairness issue among sensor nodes. Because we only apply the greedy feature based on the TSP tour, the potential unfairness issue is significantly alleviated.

How Much to Charge:                   

To achieve the energy synchronization among nodes, the charger does not always fully charge individual nodes, and then, we explain how to determine the amount of energy charged to the selected node. In our design, we determine the amount of energy charged to individual nodes with the objective of synchronizing their energy supply. Thus, given the selected charging node, we first need to identify the node to which to synchronize its energy, i.e., its synchronization target.

Periodic Charging Process:

Next we will show that the aforementioned mobile charging protocol leads to a periodic sequence of the TSP tour selected under the ideal case.Consider the ESync protocol, where the amount of energy charged is computed by (6). Assuming that the energy consumption rater(u)is perfectly estimated and ignoring the worst-case charging time tc, the selected TSP tours converge to be periodic.

Proof: We model the TSP selection and energy charging process as a Markov decision process (MDP). The state of the MDP is the remaining energy er(u), and the action is the amount of energy chargede(s). The transition probabilities are either 1 or 0 sincee(s)is computed by (6), at each rounder(u) is a multiple of r(u), i.e., there exists an integer βsuch that er(u)=βr(u). On the other hand, er(u)≤E,whereEis the node capacity when fully charged. Therefore, the state space of the MDP is finite. Ignoring the worst-case charging time tc, the action space of the MDP is also finite by (6). There must exist some states that are recurrently reached, which completes the proof.

Performance Evaluation

We also conducted simulation by using NS2 and experimental results show that our approach outperforms in the terms of Total Charging Delay and Travelling Distance.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Lingkun Fu, Member, IEEE, LiangHe, Member, IEEE, Peng Cheng, Member, IEEE, Yu Gu, Member, IEEE, Jianping Pan, Senior Member, IEEE, and Jiming Chen, Senior Member, IEEE, “ESync: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks” IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 65, NO. 9, SEPTEMBER 2016.

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

ABSTRACT:

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

  • Existing systems are formulate congestion alleviation as a bi-level programming problem. The lower-level problem predicts how drivers will react to any given signal control pattern. The upper-level problem is to determine signal splits to optimize a system objective function of drivers ‘route choice behavior in response to signal split. However, this framework is derived based on the assumption of equilibrium flows, which is not realistic because traffic is always much more dynamic and complex. Besides,
  • Li et al. address the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) based on a space-phase-time hyper network. In this framework, each vehicle is guided on a path and the traffic signals servicing those vehicles are set to minimize their travel time, which is finally decoupled and solved by the Lagrangian Relaxation optimization frame-work. In addition, Bazzanet al. propose an evolutionary algorithm to try out a large number of candidate solutions (i.e., diversified routes and traffic signals), and find solution with the best fitness based on simulation. However, this approach does not optimize the two components in a simultaneous manner under a unified framework.

PROPOSED SYSTEM:

We propose a multiagent traffic management framework in this paper, which unifies vehicle rerouting and traffic light control by the notion of digital “pheromone,” with the novelty and contributions summarized as follows:

  • we are the first to use a pheromone-based method to combine vehicle rerouting and traffic light control, to alleviate traffic congestion.
  • we propose to utilize the digital pheromone involving drivers’ route intention in predicting the short-term traffic conditions;.
  • we propose a proactive pheromone-based vehicle rerouting algorithm to direct vehicles away before they enter congested roads.
  • the proposed traffic management framework is a decentralized approach, which is applicable to large scale road networks.

DISADVANTAGES OF EXISTING SYSTEM:

  • Frail on complex road networks
  • Doesn’t decrease congestion levels on road networks
  • More fuel consumption &
  • Doesn’t gave proper solution to air polluted system

ADVANTAGES OF PROPOSED SYSTEM:

  • Experimental results on two road networks show the superior performance in terms of congested road units, road coverage rate, arrived vehicles, travel time, air pollution and fuel consumption.
  • In addition, our traffic management framework is robust to various compliance and penetration rates.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System :  Pentium IV 2.4 GHz.
  • Hard Disk : 40 GB.
  • Monitor : 15 inch VGA Colour.
  • Mouse : Logitech Mouse.
  • Ram : 512 MB
  • Keyboard : Standard Keyboard

SOFTWARE REQUIREMENTS:-

  • Operating System : LINUX
  • Tool : Network Simulator-2
  • Front End : OTCL (Object Oriented Tool Command  Language)

REFERENCE:

Zhiguang Cao, Siwei Jiang, Jie Zhang, and Hongliang Guo, IEEE “A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion”- IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS.

Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks

Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks

ABSTRACT:

We propose and analyze adaptive network defense management for countering smart attack and selective capture which aims to cripple the basic data delivery functionality of a base station based wireless sensor net-work. With selective capture, the adversaries strategically capture sensors and turn them into inside attackers. With smart attack, an inside attacker is capable of performing random, opportunistic and insidious attacks to evade detection and maximize their chance of success. We develop a model-based analysis methodology with simulation validation to identify the best defense protocol settings under which the sensor network lifetime is maximized against selective capture and smart attack.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram :1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Hamid Al-Hamadi and Ing-Ray Chen, Member, IEEE, “Adaptive Network Defense Management for Countering Smart Attack and Selective Capture in Wireless Sensor Networks”, IEEE Transactions on Network and Science Management, 2016.

Virtual Multipath Attack and Defense for Location Distinction in Wireless Networks

Virtual Multipath Attack and Defense for Location Distinction in Wireless Networks

Virtual Multipath Attack and Defense for Location Distinction in Wireless Networks

ABSTRACT:

In wireless networks, location distinction aims to detect location changes or facilitate authentication of wireless users. To achieve location distinction, recent research has focused on investigating the spatial uncorrelation property of wireless channels. Specifically, differences in wireless channel characteristics are used to distinguish locations or identify location changes. However, we discover a new attack against all existing location distinction approaches that are built on the spatial uncorrelation property of wireless channels. In such an attack, the adversary can easily hide her location changes or impersonate movements by injecting fake wireless channel characteristics into a target receiver. To defend against this attack, we propose a detection technique that utilizes an auxiliary receiver or antenna to identify these fake channel characteristics. We also discuss such attacks and corresponding defenses in OFDM systems. Experimental results on our USRP-based prototype show that the discovered attack can craft any desired channel characteristic with a successful probability of 95.0% to defeat spatial uncorrelation based location distinction schemes and our novel detection method achieves a detection rate higher than 91.2% while maintaining a very low false alarm rate.

EXISTING SYSTEM:

Existing location distinction approaches have been focused on exploiting the spatial uncorrelation property of wireless channels. These approaches demonstrated their success in various wireless scenarios, especially for the high-frequency systems (e.g., WiFi networks) that feature a very short electromagnetic wavelength. However, two recent studies identified a vulnerability of these approaches discovered that the wireless spatial uncorrelation property may be violated in a poor multipath environment (e.g., strong line-of-sight path).The work which made a further attempt to attack location distinction systems using channel impulse responses. The authors found that a third-party attacker may impersonate Alice to Bob by mimicking the channel impulse response of the wireless link between them, and the authors named such attacks as mimicry attacks. Although both mimicry attacks and the virtual multipath attacks are against the security measures based on the wireless channel characteristics, they differ from each other in the following aspects.

DISADVANTAGES OF EXISTING SYSTEM:

  • Conventional attack scenario is considered
  • Lack of accuracy in new attack scenario.
  • Detection of attack is more complex.

PROPOSED SYSTEM:

We propose a detection technique utilizing an auxiliary receiver (or antenna) at a different location to identify the virtual multipath channels and the fake channel characteristics. Specifically, the attacker must craft its transmitting signal to make the target receiver believe a particular channel characteristic. Our contributions are summarized as follows.

 We discover that multipath propagation can be artificially made in a lab environment, and create a technique that can successfully generate virtual multipath channels.

Based on the virtual multipath channel, we identify a new type of attack that can defeat all existing location distinction algorithms using the spatial uncorrelated property of wireless channels.

We create a defense technique to detect such attacks and protect location distinction systems. We specifically explore such attacks in OFDM systems and craft corresponding defenses according to the objective of attackers.

We implement real-world prototypes to examine the practical impact of the attacks and the effectiveness of the proposed defense method.

ADVANTAGES OF PROPOSED SYSTEM:

  • New attack scenario is introduced.
  • Detection rate is satisfactory when compared to other systems.
  • High accuracy rate on detection of attack.

SYSTEM ARCHITECTURE:

Virtual Multipath Attack and Defense for Location

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Song Fang, Yao Liu, Wenbo Shen, Haojin Zhu and Tao Wang, “Virtual Multipath Attack and Defense for Location Distinction in Wireless Networks”, IEEE Transactions on Mobile Computing, 2016.

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

ABSTRACT:

We address the problem of preventing the inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50%; and the end-to-end delay by more than 30%. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

  • The problem of preserving contextual information privacy has been studied under various adversarial scenarios. Threat models can be classified based on the adversary’s network view (local vs. global) or the capabilities of the eavesdropping devices (packet decoding, localization of the transmission source, etc.).
  • Under a local model, eavesdroppers are assumed to intercept only a fraction of the WSN traffic. Hiding methods include random walks, adding of pseudo-sources and pseudo-destinations, creation of routing loops, and flooding.
  • These methods can only provide probabilistic obfuscation guarantees, because eavesdroppers locations are unknown. Under a global model, all communications within the WSN are assumed to be intercepted and collectively analyzed.

DISADVANTAGES OF EXISTING SYSTEM:

  • First, eavesdroppers are passive devices that are hard to detect.
  • Second, the availability of low-cost commodity radio hardware makes it inexpensive to deploy a large number of eavesdroppers.
  • Third, even if encryption is applied to conceal the packet payload, some fields in the packet headers still need to be transmitted in the clear for correct protocol operation (e.g., PHY-layer headers used for frame detection, synchronization, etc.). These unencrypted fields facilitate accurate estimation of transmission attributes.
  • High communication overhead and increased end-to-end delay for reporting events.

PROPOSED SYSTEM:

  • We study the problem of resource efficient traffic randomization for hiding contextual information in event-driven WSNs, under a global adversary.
  • Our main contributions are summarized as follows:
  • We present a general traffic analysis method for inferring contextual information that is used as a baseline for comparing methods with varying assumptions.
  • Our method relies on minimal information, namely packet transmission time and eavesdropping location.
  • We propose traffic normalization methods that hide the event location, its occurrence time, and the sink location from global eavesdroppers.
  • Compared to existing approaches, our methods reduce the communication and delay overheads by limiting the injected bogus traffic. This is achieved by constructing minimum connected dominating sets (MCDSs) and MCDSs with shortest paths to the sink (SSMCDSs).
  • We characterize the algorithmic complexity for building SS-MCDSs and develop efficient heuristics.
  • To reduce the forwarding delay, we design a rate control scheme that loosely coordinates sensor transmissions over multi-hop paths without revealing real traffic patterns or the traffic directionality.

ADVANTAGES OF PROPOSED SYSTEM:

  • The proposed system reduces the communication and delay overheads by limiting the injected bogus traffic.
  • The proposed system reduces the forwarding delay
  • We compare privacy and overhead of our techniques to prior art and show the savings achieved.

SYSTEM ARCHITECTURE:

Traffic Decorrelation Techniques for Countering

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Alejandro Proa˜no, Loukas Lazos, and Marwan Krunz, “Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs”, IEEE TRANSACTIONS ON MOBILE COMPUTING 2016

Towards Distributed Optimal Movement Strategy for Data Gathering in Wireless Sensor Networks

Towards Distributed Optimal Movement Strategy for Data Gathering in Wireless Sensor Networks

Towards Distributed Optimal Movement Strategy for Data Gathering in Wireless Sensor Networks

ABSTRACT:

In this paper, we address how to design a distributed movement strategy for mobile collectors, which can be either physical mobile agents or query/collector packets periodically launched by the sink, to achieve successful data gathering in wireless sensor networks. Formulating the problem as general random walks on a graph composed of sensor nodes, we analyze how much data can be successfully gathered in time under any Markovian random-walk movement strategies for mobile collectors moving over a graph (or network), while each sensor node is equipped with limited buffer space and data arrival rates are heterogeneous over different sensor nodes. In particular, from the analysis, we obtain the optimal movement strategy among a class of Markovian strategies so as to minimize the data loss rate over all sensor nodes, and explain how such an optimal movement strategy can be made to work in a distributed fashion. We demonstrate that our distributed optimal movement strategy can lead to about two times smaller loss rate than a standard random walk strategy under diverse scenarios. In particular, our strategy results in up to 70 percent cost savings for the deployment of multiple collectors to achieve the target data loss rate than the standard random walk strategy.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

  • As to the random walk-based routing for data gathering, the existing research studies have mainly focused on the performance of the following metrics: delay—the time for a random walk (a data or query packet) to reach its destination (a sink node or a sensor having certain information of interest), and cover time or its partial cover time—the time for the random walk until to visit all or partial set of sensors. These metrics are suitable for one shot information delivery or search/query.
  • Also, the random walk-based data gathering is typically for delay-insensitive applications in which the collected data is mainly used for post-processing or other research studies later. It is thus more important to measure how much data can be collected before it is lost due to limited buffer space, when the sink periodically generates query packets or collector packets moving over the network in a random walk fashion to gather measured data or its aggregated/compressed version from sensor nodes.

DISADVANTAGES OF EXISTING SYSTEM:

  • Improper way of data gathering using random walk.
  • Some collected data are lost due to its restricted buffer space.
  • High consumed battery power when mobile agents are utilized.
  • High examination over global network information like sensor distance, location information etc.

PROPOSED SYSTEM:

  • We develop an analytical framework to evaluate the network loss probability under any Markovian random walk strategy.
  • From the framework, we obtain the distributed optimal movement strategy for mobile collectors requiring only local information so as to minimize the network loss probability, which is essentially to come to each sensor i with long term visit frequency (stationary distribution) pi where pi is the data arrival rate to sensor i and B is the buffer size at sensors. Here the distributed implementation is made possible via the famous Metropolis Hastings (MH) algorithm.
  • We then demonstrate that our distributed optimal movement strategy leads to remarkable performance improvement over the standard random walk strategy under various settings of network topology, buffer size, and the number of deployed mobile agents, in addition to diverse data arrival scenarios in the sensor field.

ADVANTAGES OF PROPOSED SYSTEM:

  • Random walk is performed without use of global information.
  • Our strategy reduces the network loss probability by about 50 percent, while at the same time becoming more cost-effective in term of the required buffer size or the required number of mobile collectors to achieve a target loss probability under various scenarios.
  • The amount of reduction in the network loss probability that we achieve from our distributed optimal movement strategy (in comparison with the standard random walk strategy) also becomes larger for the increased number of nodes.

SYSTEM ARCHITECTURE:

Towards Distributed Optimal Movement Strategy

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Chul-Ho Lee, Jaewook Kwak, and Do Young Eun, “Towards Distributed Optimal Movement Strategy for Data Gathering in Wireless Sensor Networks”, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 27, NO. 2, FEBRUARY 2016.

Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs

Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs

ABSTRACT:

In mobile ad hoc networks (MANETs), it is effective to retrieve data items using top-k query. However, accurate results may not be acquired in environments when malicious nodes are present. In this paper, we assume that malicious nodes attempt to replace necessary data items with unnecessary ones (we call these data replacement attacks), and propose methods for top-k query processing and malicious node identification based on node grouping in MANETs. In order to maintain the accuracy of the query result, nodes reply with k data items with the highest score along multiple routes, and the query-issuing node tries to detect attacks from the information attached to the reply messages. After detecting attacks, the query-issuing node tries to identify the malicious nodes through message exchanges with other nodes. When multiple malicious nodes are present, the query-issuing node may not be able to identify all malicious nodes at a single query. It is effective for a node to share information about the identified malicious nodes with other nodes. In our method, each node divides all nodes into groups by using the similarity of the information about the identified malicious nodes. Then, it identifies malicious nodes based on the information on the groups. We conduct simulation experiments by using a network simulator, QualNet5.2, to verify that our method achieves high accuracy of the query result and identifies malicious nodes.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

  • Existing System such as secure top-k query processing methods in the environment where there are some malicious nodes in a network, the authors have proposed a method in which each sensor node sends each data item attached both the hash value of one priority data item and that of one superior data item. After the source node received the top-k result, it ensures the safety of the received data items to check whether the received hash values correspond with hash values calculated by the received data items. In these methods, the sender node protects against fabrication of data items by sending data items encrypting with a symmetric key. However, these methods cannot handle DRAs.
  • Especially, the authors have proposed a method against false data injection attacks, where malicious nodes generate new and false data items (i.e., other nodes’ data items or data items whose score are not same as the score calculated from raw data items and query conditions) and send back them. However, we assume that raw data items are generated from some special devices and software’s such as medical sensors, which can be read but cannot be modified even by the owner nodes.

PROPOSED SYSTEM:

  • We describe a new attack model, DRA, in which a malicious node replaces necessary data items with unnecessary ones, and we analyze the effects of such an attack on top-k query processing when there are multiple malicious nodes in the networks.
  • We propose methods for processing top-k queries and for identifying malicious nodes against a DRA in MANETs.
  • We describe an attack model, FNA, in which a malicious node sends fake information that claims some normal nodes as malicious nodes, and we evaluate the effects of such an attack.
  • We verify that our proposed methods can achieve high accuracy of the query result and identify malicious nodes, through extensive simulations that take into account physical layer effects in the networks.

ADVANTAGES OF PROPOSED SYSTEM:

  • Accurate malicious node detection
  • High accuracy of query result

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

TAKUJI TSUDA, YUKA KOMAI, TAKAHIRO HARA, (Senior Member, IEEE), AND SHOJIRO NISHIO, (Fellow, IEEE), “Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs”, IEEE ACCESS, 2016.

Thwarting Selfish Behavior in 802.11 WLANs

Thwarting Selfish Behavior in 802.11 WLANs

Thwarting Selfish Behavior in 802.11 WLANs

ABSTRACT:

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper, we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for stations to react, upon detecting a misbehavior, by using a more aggressive configuration that penalizes the misbehaving station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting an analysis of the effectiveness of the algorithm against selfish behaviors, we also show that a misbehaving station cannot obtain any gain by deviating from the algorithm. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.

EXISTING SYSTEM:

  • The Existing approach is based on selective jamming: If a station detects that another station is misbehaving, there-after it listens to its transmitted packets and switches to trans-mission mode, jamming enough bits so that the packets cannot be properly recovered at the receiver. While the use of jamming punishes misbehaving stations, it has the major drawback of relying on functionality not available in current wireless devices.
  • Indeed, due to the accurate timing required, the implementation of such a mechanism would need to be performed at the hard-ware level and entails substantial complexity.
  • The other existing method does not suffer from the above drawback, but addresses only two types of misbehaving stations: the so-called selfish stations, and So-called greedy stations, While the scheme proposed is effective when dealing with these two particular con-figurations, other CW configurations that may greatly benefit a misbehaving station are neither detected nor punished by this mechanism .Additionally, the algorithm of is based on heuristics that do Not guarantee quick convergence, and that this approach may suffer from convergence issues.

DISADVANTAGES OF EXISTING SYSTEM:

  • It Suffer from convergence issues
  • It is more complex
  • Vulnerable to attacks

PROPOSED SYSTEM:

  • We propose a novel distributed algorithm that penalizes misbehaving stations by making use of a more aggressive configuration of the 802.11e parameters upon detecting misbehavior.
  • We conduct a stability analysis of the algorithm to show that when all stations implement our algorithm, the WLAN convergences to the optimal point of operation.
  • We conduct an analysis of the effectiveness of the algorithm against selfish behavior that shows that a station cannot increase its throughput by deviating from the algorithm.
  • We extensively evaluate the performance of the proposed algorithm via simulation under a wide variety of conditions that confirm its good properties.
  • We show the feasibility of implementing the algorithm by deploying a prototype and evaluating it in a small experimental testbed.

ADVANTAGES OF PROPOSED SYSTEM:

  • It provides quick convergence
  • It is simple
  • The System is guarded

SYSTEM ARCHITECTURE:

Thwarting Selfish Behavior in 802.11 WLANs

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Albert Banchs, SeniorMember, IEEE, Jorge Ortin, Andres Garcia-Saavedra, Douglas J. Leith, SeniorMember, IEEE, and Pablo Serrano, Member, IEEE , “Thwarting Selfish Behavior in 802.11 WLANs”, IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 24, NO. 1, FEBRUARY 2016.

TCP-Aware Backpressure Routing and Scheduling

TCP-Aware Backpressure Routing and Scheduling

TCP-Aware Backpressure Routing and Scheduling

ABSTRACT:

In this work, we explore the performance of backpressure routing and scheduling for TCP flows over wireless networks. TCP and backpressure are not compatible due to a mismatch between the congestion control mechanism of TCP and the queue size based routing and scheduling of the backpressure framework. We propose a TCP-aware backpressure routing and scheduling mechanism that takes into account the behavior of TCP flows. TCP-aware backpressure provides throughput optimality guarantees in the Lyapunov optimization framework, and gracefully combines TCP and backpressure without making any changes to the TCP protocol. The simulation results show that TCP-aware backpressure (i) improves the throughput of TCP flows significantly, (ii) provides fairness across competing TCP flows, and (iii) accommodates both TCP and non-TCP flows in a wireless network, and improves throughput of these flows without hurting fairness.

PROJECT OUTPUT VIDEO: (Click the below link to see the project output video):

EXISTING SYSTEM:

Maximum weight matching (MWM) is a switch scheduling algorithm and has similar properties as the max-weight scheduling algorithm and backpressure. Similar to the backpressure, there is incompatibility between TCP and MWM. Yet, we consider backpressure routing and scheduling over wireless networks rather than switch scheduling, and we take a holistic approach to address this problem; i.e., we propose TCP-aware backpressure to make TCP and backpressure compatible. The delay-based routing and scheduling algorithms can be also be utilized with TCP flows. However, the delay-based solutions have two disadvantages in this setup. First, providing performance guarantees for delay-based algorithms are quite involved and is an open problem for general networks. Furthermore, they require clock synchronization, which is quite difficult in practice. As compared to this line of work, we propose TCP-aware backpressure with provable performance guarantees. Also, since TCP-aware backpressure does not introduce complications such as clock synchronization, or updating TCP, it is very suitable for practical deployment

DISADVANTAGES OF EXISTING SYSTEM:

  • Less throughput of the network.
  • More packet losses in the network.
  • Consume more end-to-end delay.

PROPOSED SYSTEM:

We identify the mismatch between TCP and the backpres-sure framework; i.e., their joint behavior is so detrimental that some flows may never get a chance to transmit. In order to address the mismatch between TCP and backpressure, we develop “TCP-aware backpressure routing and scheduling”.

We show that (i) TCP-aware backpressure routing and scheduling stabilizes queues for any feasible traffic as the classical backpressure, (ii) TCP-aware back-pressure routing and scheduling provides the same utility-optimal operation guarantee when combined with a flow control algorithm as the classical backpressure .

We provide implementation details and explain how to tune TCP-aware backpressure so that it complies with TCP. Moreover, we combine network coding and TCP-aware backpressure to address the additional challenges such as out of order delivery, packet loss, and jitter. Thanks to employing network coding, which makes TCP flows sequence agnostic (with respect to packet IDs), TCP-aware backpressure fully complies with TCP.

We develop a TCP-friendly flow control mechanism, which when combined with TCP-aware backpressure, accommodates both TCP and non-TCP flows in a wireless network. In this setup, TCP-aware backpressure improves throughput of both TCP and non-TCP flows and provides fairness across competing flows.

We evaluate our schemes in a multi-hop setting, using ns-2. The simulation results (i) confirm the mismatch ofTCP and backpressure, (ii) show that TCP-aware back-pressure is compatible with TCP, and significantly im-proves throughput as compared to existing adaptive routing schemes, (iii) demonstrate that TCP-aware backpressure provides fairness across competing TCP flows, (iv) show that both TCP and non-TCP flows can be accommodated in wireless network with TCP-aware backpressure, and throughput is improved without hurting fairness.

ADVANTAGES OF PROPOSED SYSTEM:

  • Improve throughput of the network.
  • Decrease packet losses in the network.
  • Less end-to-end delay.

SYSTEM ARCHITECTURE:

TCP-Aware Backpressure Routing and Scheduling

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows XP/UBUNTU.
  • Implementation : NS2
  • NS2 Version : 2.28
  • Front End : OTCL (Object Oriented Tool Command  Language)
  • Tool : Cygwin (To simulate in Windows OS)

REFERENCE:

Hulya Seferoglu, Member, IEEE, Eytan Modiano, Fellow, IEEE, “TCP-Aware Backpressure Routing and Scheduling”, IEEE Transactions on Mobile Computing, 2016.