Multipath Routing Using Anti Jamming Method

Published Date: 02 Nov 2017

Introduction

Literature

Existing System

Proposed System

Module Description

Summary

References

OUTLINE OF THE PROPOSAL

To distribute the data from source node multiple path source routing will allow the total traffic among available paths. In this, we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization. The main contributions of this work are summarized as follows. This work offers a comprehensive survey as well as the results of typical jamming attacks at various aspects and their application on sensor networks. This work suggests the most efficient models and demonstrates the performance gain for both synthetic and real data.

INTRODUCTION

With the appealing characteristics of low-cost, easy to deploy, and unattended operation and the ability of withstanding harsh environmental conditions, wireless sensor networks have been implemented in a wide range of applications, such as environment monitoring and event detection. Sensor networks transmit wireless signals over the open shared media. This leaves a sensor network vulnerable to radio jamming attacks. From last several years several jamming attacks have been explored, which corrupt control packets, such as RTS (Request-to-Send) and CTS (Clear-to-Send). The jammer just keeps sending packets like RTS to prevent transmission of legitimate packets. These methods are usually based on the statistics of packet transmission history and can cause severe damage to the sensor network with only modest overhead.

Thus, antijamming is enormously important for secure operation of sensor networks. As being well known, sensor nodes are typically powered by batteries and hence limited in power supply. This has been generally accepted as one of the crucial issues of the sensor network. Therefore, antijamming needs to be energy efficient.

A number of antijamming methods have been proposed, such as channel surfing [5], error correction codes and transmission power adjustment. However, these existing countermeasures of jamming attacks are usually suitable for a limited range of jamming conditions with varying operation cost. In the real world scenario, jamming attacks may be very different in nature and may change over time. In addition, radio signals are unstable as many factors may cause jamming signal attenuated in different ways for different environments. As a result, different nodes suffer different degrees of radio jamming. Thus, it is inefficient for a whole sensor network simply to apply a single antijamming technique. This may result in poor performance of antijamming and/or still suffer serious performance degradation of energy consumption.

LITERATURE

Since jamming attacks have been recognized as a crucial issue in sensor networks, a plenty of research has been conducted. In this section we survey different jamming attacks and counter measures against jamming in sensor networks.

Nischay Bahl et.al[1] considered a particular class of DoS attacks called jamming attacks using IEEE 802.15.4 based OPNET simulative model for WSN, under constant and varying intensity of attacks. The effects of the number of attackers on SNR, BER, network throughput and PDR are inclusively evaluated for simulated scenarios. In the simulation design they used the IEEE 802.15.4 based star topology spread over the region 100x100m wide including analyzer node, PAN coordinator and sensing nodes. The normal scenario design consisted of one analyzer node, one Full Function Device (FFD) - PAN coordinator and 16 wireless sensor nodes (Reduced Function Device (RFD)). Jammed scenario design consisted of one analyzer node, one FFD node (PAN coordinator) and 16 wireless sensor nodes (including malicious nodes). Different scenario designs used varying intensity of jamming attack. Malicious nodes used JAMMOD modulation, whereas, the normal sensor nodes used Quadrature-phase-Shift-Keying (QPSK) modulation. The JAMMOD modulation technique causes simulation run to interpret all traffic with interference or noise which degrades the network performance.

Further they investigated the security aspects of the physical layer. They conducted the simulative performance analysis of jamming attacks for signal-to-noise ratio (SNR), bit error rate (BER), network throughput and packet delivery ratio (PDR) using IEEE 802.15.4 based OPNET simulative model for WSN under constant and varying intensity of jamming attacks. Under constant jamming attack, simulations revealed that average sink node PDR degrades from 79.01% in a normal scenario, to 59.22% in jammed scenario. Also, normal scenario shows maximum PDR of 89.68% and minimum PDR of 70.02% while jammed scenario shows a maximum PDR of 64.93% and minimum PDR of 49.90%. Under varying intensity of jamming attack, simulations revealed that average sink node PDR decreases, from 79.01% in a normal scenario, by 5.54%, 4.53%, 6.36% and 3.35% with the introduction of one, two, three and four jammers respectively. Further, the average SNR decreases, from 73.59%, in a normal scenario, by 5.43%, 5.63%, 10.44% and 20.39% with the introduction of one, two, three and four jammers respectively.

Matthias Wilhelm et.al[2] demonstrates that flexible and reliable software-defined reactive jamming is feasible by designing and implementing a reactive jammer against IEEE 802.15.4 networks. First, identify the causes of loss at the physical layer of 802.15.4 and show how to achieve the best performance for reactive jamming. Then, apply these insights to our USRP2-based reactive jamming prototype, enabling a classification of transmissions in real-time, and reliable and selective jamming. The prototype achieves a reaction time in the order of microseconds, a high precision (such as targeting individual symbols), and a 97.6% jamming rate in realistic indoor scenarios for a single reactive jammer, and over 99.9% for two concurrent jammers.

Finally justified that real-time reactive jamming based on the software-defined radio paradigm is feasible and must be considered a realistic threat. Here analysis is based on a prototype implementation, which achieves a high precision of reactive jamming even if using low-cost COTS hardware such as the USRP2. Using this prototype, It provided insights to the causes for loss, and offered guidelines for successful reactive jamming against WSNs with an experimental study on physical layer effects. Finally evaluated the performance of prototype system in a realistic MICAz testbed, and showed that the proposed system design offers not only a high precision but also the possibility of adapting the system to new requirements, such as reactively jamming 802.11 networks.

Matthias Wilhelm et.al[3] present RFReact, an USRP2-based jamming platform that enables selective and reactive jamming. Built-in RF analyzers detect signals of interest and trigger transmissions of jamming waveforms. RFReact benefits from the full access to physical layer information due to its software-defined radio implementation: it is agnostic to technology standards and readily adaptable to various applications. Examples of such applications are the controlled and repeatable generation of RF interference to experimentally evaluate the robustness of protocols, the evaluation of reactive jamming strategies and possible countermeasures, and the generation of precisely timed transmissions for experiments. It emphasized the ability to adapt and extend RFReact to different applications. RF analyzers can be added to search for new features of interest on the wireless channel. As input, such an analyzer receives complex samples from the radio frontend to apply their signal processing on. As output, interrupts are used to interact with the firmware for event notification and a bus interface is used to exchange arbitrary data between the analyzer and the rest of the system.

The transmission scheduling module is part of the microcontroller’s interrupt controller, and is able to request additional information on a detected event via the bus interface. This information is then used to make scheduling decisions, specifying the behavior of RFReact. Defining waveforms to be transmitted by the system is a matter of giving a sequence of complex samples that represent the waveform. In this way, modulated data can be transmitted to communicate with the rest of the network. Finally demonstrate that RFReact is both versatile and precise with a reactive jammer that can demodulate large parts of 802.15.4 packets, decide whether to jam them or not, and execute the decisions while the packets are still on the air.

Zhenhua Liu et.al [4] focuses on developing mechanisms to localize a jammer. It first conducts jamming effect analysis to examine how a hearing range, e.g., the area from which a node can successfully receive and decode the packet, alters with the jammer's location and transmission power. Then, it show that the affected hearing range can be estimated purely by examining the network topology changes caused by jamming attacks. As such, we solve the jammer location estimation by constructing a least-squares problem, which exploits the changes of the hearing ranges. To evaluate the accuracy of localizing the jammer, it defines the localization error as the Euclidean distance between the estimated jammer's location and the true location. To capture the statistical characteristics, it studied the average errors under multiple experimental rounds and we presented both the means and the Cumulative Distribution Functions (CDF) of the localization error.

This approach does not depend on measuring signal strength inside the jammed area, nor does it require delivering information out of the jammed area. Thus, it works well in the jamming scenarios where network communication is disturbed. Additionally, compared with prior work which involves searching for the location of the jammer iteratively, our hearing-range-based algorithm finishes the location estimation in one step, significantly reducing the computation cost while achieving better performance.

Wenyuan Xu [5] examines issues associated with using power control both theoretically and experimentally. It begins by examining the two party, single-jammer scenario, where we explore the underlying communication theory associated with jamming. It note that the effect of the jammer upon source-receiver communications is not isotropic. We then discuss the potential for improving communication reliability through experiments conducted using Mica2 motes, and in particular explore the feasibility of power-control for competing against jammers. Next, turn to examining the more complicated scenario consisting of a multi-hop wireless network. Though study of the two-node network scenario reveals important insights associated with adapting the transmit power to cope with jamming, a study of the more general multihop network scenario carries more practical significance. In particular, the multihop network scenario, which corresponds to ad hoc and sensor network deployments, inherently involves more complicated interaction between network participants. Each node may have one or more neighbors and each node form links to their neighbor depending on the distance to their neighbors, their transmission power and the abient noise around. We will show that a dynamical power control protocol is necessary to adapt to the network topology. Finally show the complex jamming effect by applying the non-isotropic model of jamming to a multi-hop wireless network, and it is necessary to have a feed-back based power control protocol to compete withjamming interference.

K. Siddhabathula et.al [6] propose collaborative detection, which evaluates the packet delivery ratio in an given area instead of a pair of nodes. The intuition is that the attacker often jams an area of his interest, not just two specific nodes. The benefit is that can detect jamming attacks in a much faster way. The protocol was tested against one constant jammer which was placed at various positions and at different orientation to jam different number of motes for each position and orientation of the jammer. The jammer was placed outside the perimeter of the network. The range of the TelosB motes is reduced in an indoor environment. To calculate the detection time, one of the non-jammed mote was connected to the computer. Whenever that mote received an alert message it would print the reception time on the system. This gave us the time at which detection was complete. To get the time when the attack was launched, another mote was also connected to the computer which sent a message to the jammer on reception of which the jammer starts the attack on the network. That mote will print the time when the jammer is launching the attack on the system screen. By taking the difference between the two times we get the detection time. For the detection time, the length of time intervals are configured in five different ways, i.e., five sets of experiments are conducted, each having different interval length. Finally it have evaluated the performance of our idea on TelosB motes. The results show that we can effectively and quickly detect jamming attacks.

X. Jiang et.al [7] propose a compromise-resilient anti-jamming scheme called split-pairing scheme to deal with single insider jamming problem in a one-hop network setting. Proposed scheme can be extended to the multiple-jammer case. In this case, split the network (excluding the jammer) into N1 groups where N1 > number of jammers and each group has a leader whose id is publicly determined. Next, each group leader tries to distribute the key K to its group members. Because N1 >number of jammers, at least one group (called recovered group) will be able to share K to all its group members. In the pairing phase, one-to-one pairing is replaced by one-to-many pairing where we pair one node from the recovered group with nodes each from each subgroup to be recovered. Again, a multivariate version of the Blundo scheme can be used to calculate a pairing key for each set of nodes only given the node ids as the input. Finally, a pairing key is used to select a new communication channel and securely deliver K to the other nodes in each paired group. Through these three similar phases, all the nodes will know the same global key K.

This article gave a view of jamming attacks models as well as different programming models proposed by different authors in sensor networks. In this we say that, this study will help the researchers to develop the better techniques in the field of jamming in sensors environment.

Existing System:

In order to characterize the effect of jamming on throughput, each source must collect information on the impact of the jamming attack in various parts of the network. However, the extent of jamming at each network node depends on a number of unknown parameters, including the strategy used by the individual jammers and the relative location of the jammers with respect to each transmitter–receiver pair. Hence, the impact of jamming is probabilistic from the perspective of the network, and the characterization of the jamming impact is further complicated by the fact that the jammers’ strategies may be dynamic and the jammers themselves may be mobile.

In order to capture the nondeterministic and dynamic effects of the jamming attack, we model the packet error rate at each network node as a random process. At a given time, the randomness in the packet error rate is due to the uncertainty in the jamming parameters, while the time variability in the packet error rate is due to the jamming dynamics and mobility.

Disadvantages:

disturb wireless communications

proactive / reactive

constant, random, repeat, deceive

single bit/packet

outsider / insider

Time Delay

Proposed System

In this, we thus investigate the ability of network nodes to characterize the jamming impact and the ability of multiple source nodes to compensate for jamming in the allocation of traffic across multiple routing paths. Our contributions to this problem are as follows.

We formulate the problem of allocating traffic across multiple routing paths in the presence of jamming as a lossy network flow optimization problem. We map the optimization problem to that of asset allocation using portfolio selection theory.

We formulate the centralized traffic allocation problem for multiple source nodes as a convex optimization problem.

We show that the multisource multiple-path optimal traffic allocation can be computed at the source nodes using a distributed algorithm based on decomposition in network utility maximization (NUM).

We propose methods that allow individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes.

We demonstrate that the use of portfolio selection theory allows the data sources to balance the expected data throughput with the uncertainty in achievable traffic rates.

Advantage: Each time a new routing path is requested or an existing routing path is updated, the responding nodes along the path will relay the necessary parameters to the source node as part of the reply message for the routing path.

Goal: Efficiently allocate the traffic to maximize the overall throughput.

Modules Description

In this we divide the proposed system into following modules

Allocation of traffic across multiple routing paths

Characterizing The Impact Of Jamming

Effect of Jammer Mobility on Network

Estimating End-to-End Packet Success Rates

Optimal Jamming-Aware Traffic Allocation

Allocation of traffic across multiple routing paths: We formulate the problem of allocating traffic across multiple routing paths in the presence of jamming as a lossy network flow optimization problem. We map the optimization problem to that of asset allocation using portfolio selection theory which allows individual network nodes to locally characterize the jamming impact and aggregate this information for the source nodes.

Characterizing The Impact Of Jamming: In these Module the network nodes to estimate and characterize the impact of jamming and for a source node to incorporate these estimates into its traffic allocation. In order for a source node s to incorporate the jamming impact in the traffic allocation problem, the effect of jamming on transmissions over each link must be estimated. However, to capture the jammer mobility and the dynamic effects of the jamming attack, the local estimates need to be continually updated.

Effect of Jammer Mobility on Network: The capacity indicating the link maximum number of packets per second (pkt/s) eg:200 pkts/s which can be transported over the wireless link. Whenever the source is generating data at a rate of 300 pkts/s to be transmitted at the time jamming to be occurring. Then the throughput rate to be less. If the source node becomes aware of this effect the allocation of traffic can be changed to 150 pkts/s on each of paths thus recovers the jamming path.

Estimating End-to-End Packet Success Rates: The packet success rate estimates for the links in a routing path, the source needs to estimate the effective end-to-end packet success rate to determine the optimal traffic allocation. Assuming the total time required to transport packets from each source s to the corresponding destination is negligible compared to the update relay period.

Optimal Jamming-Aware Traffic Allocation: An optimization framework for jamming-aware traffic allocation to multiple routing paths for each source node. We develop a set of constraints imposed on traffic allocation solutions and then formulate a utility function for optimal traffic allocation by mapping the problem to that of portfolio selection in finance.

SUMMARY

In this, we studied the problem of traffic allocation in multiple-path routing in the presence of jammers. We have presented methods for each network node to probabilistically characterize the local impact of a dynamic jamming attack. We formulated multiple-path traffic allocation in multisource networks as a lossy network flow optimization problem using an objective function. We presented simulation results to illustrate the impact of jamming dynamics and mobility on network throughput and to demonstrate the efficiency of our proposed method. We have thus shown that multiple-path source routing can optimize the throughput performance by effectively incorporating the empirical jamming impact into the allocation of traffic to the set of paths. For future work, we intend to compare it with other multicast routing protocols, introducing new comparison metrics such as power conservation and robustness.