A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Detection and isolation of wormhole nodes in wireless ad hoc networks based on post-wormhole actions

The wormhole attack is one of the most treacherous attacks projected at the routing layer that can bypass cryptographic measures and derail the entire communication network. It is too difficult to prevent a priori; all the possible countermeasures are either too expensive or ineffective. Indeed, literature solutions either require expensive hardware (typically UWB or secure GPS transceivers) or pose specific constraints to the adversarial behavior (doing or not doing a suspicious action). The proposed solution belongs to the second category because the adversary is assumed to have done one or more known suspicious actions. In this solution, we adopt a heuristic approach to detect wormholes in ad hoc networks based on the detection of their illicit behaviors. Wormhole and post wormhole attacks are often confused in literature; that's why we clearly state that our methodology does not provide a defence against wormholes, but rather against the actions that an adversary does after the wormhole, such as packet dropping, tampering with TTL, replaying and looping, etc. In terms of contributions, the proposed solution addresses the knock-out capability of attackers that is less targeted by the researcher's community. In addition, it neither requires any additional hardware nor a change in it; instead, it is compatible with the existing network stack. The idea is simulated in ns2.30, and the average detection rate of the proposed solution is found to be 98-99%. The theoretical time to detect a wormhole node lies between 0.07-0.71 seconds. But, from the simulation, the average detection and isolation time is 0.67 seconds. In term of packet loss, the proposed solution has a relatively overhead of [Formula: see text] 22%. It works well in static and mobile scenarios, but the frame losses are higher in mobile scenarios as compared to static ones. The computational complexity of the solution is O(n). Simulation results advocate that the solution is effective in terms of memory, processing, bandwidth, and energy cost. The solution is validated using statistical parameters such as Accuracy, Precision, F1-Score and Matthews correlation coefficient ([Formula: see text]).

Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs Under Jamming Attacks

The Internet of Things (IoT) is a key technology to interconnect the real and digital worlds, enabling the development of smart cities and services. The timely collection of data is essential for IoT services. In scenarios such as agriculture, industry, transportation, public safety, and health, wireless sensor networks (WSNs) play a fundamental role in fulfilling this task. However, WSNs are commonly deployed in sensitive and remote environments, thus facing the challenge of jamming attacks. Therefore, these networks need to have the ability to detect such attacks and adopt countermeasures to guarantee connectivity and operation. In this work, we propose a novel clustering-based self-healing strategy to overcome jamming attacks, in which we denominate fairness cooperation with power allocation (FCPA). The proposed strategy, aware of the presence of the jammer, clusters the network and designates a cluster head that acts as a sink node to collect information from its cluster. Then, the most convenient routes to overcome the jamming are identified and the transmit power is adjusted to the minimum value required to guarantee the reliability of each link. Finally, through the weighted use of the relays, the lifetime of each subnetwork is extended. To show the impact of each capability of FCPA, we compare it with multiple benchmarks that only partially possess these capabilities. In the proposal evaluation, we consider a WSN composed of 64 static nodes distributed in a square area. Meanwhile, to assess the impact of the jamming attack, we consider seven different locations of the attacker. All experiments started with each node's battery full and stopped after one of these batteries was depleted. In these scenarios, FCPA outperforms all other strategies by more than 50% of the information transmitted, due to the efficient use of relay power, through the weighted balance of cooperative routes. On average, FCPA permits 967,961 kb of information transmitted and 63% of residual energy, as energy efficiency, from all the analyzed scenarios. Additionally, the proposed clustering-based self-healing strategy adapts to the change of jammer location, outperforming the rest of the strategies in terms of information transmitted and energy efficiency in all evaluated scenarios.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Low-Power Wireless Sensor Network Using Fine-Grain Control of Sensor Module Power Mode

Wireless sensor nodes are heavily resource-constrained due to their edge form factor, which has motivated increasing battery life through low-power techniques. This paper proposes a power management method that leads to less energy consumption in an idle state than conventional power management systems used in wireless sensor nodes. We analyze and benchmark the power consumption between Sleep, Idle, and Run modes. To reduce sensor node power consumption, we develop fine-grained power modes (FGPM) with five states which modulate energy consumption according to the sensor node’s communication status. We evaluate the proposed method on a test bench Mica2. As a result, the power consumed is 74.2% lower than that of conventional approaches. The proposed method targets the reduction of power consumption in IoT sensor modules with long sleep mode or short packet data in which most networks operate.