Search

Design and analysis of energy-efficient and fault-tolerant dominating set (DS) algorithms are vital tasks for Industrial Internet of Things (IIoT) scenarios, as operational efficiency is a key objective in most industries. Such algorithms must maintain topology control, clustering, routing, and data aggregation. An IIoT deployment is said to pose the self-stabilization property if it can always recover to a steady state in a bounded time without any interruption whenever it is started at an unstable state. Self-stabilization is a fruitful principle for building fault-tolerant IIoT deployments. This paper proposes a novel distributed fault-tolerant capacitated DS (capDS) algorithm for IIoT systems. The algorithm is the first self-stabilizing capDS approach to the best of our knowledge. Proofs concerning the asynchronous behaviors of the algorithm, as well as the self-stabilization feature with regard to convergence and closure properties, are provided. R2C1 Besides, our theoretical analysis showed that the algorithm’s approximation ratio is 6 for IIoT setups implemented as unit disk graphs. Measurements made using our testbed of 40 IRIS motes and the extensive TOSSIM simulations revealed that the proposed algorithm is up to 55% better in terms of coefficient of variation, requires up to 61% fewer moves, causes up to 13% less data traffic, and consumes up to 14% less energy when compared to a randomized approach and a minimum ID priority-based approach.

Drone technology is developing very rapidly. Flying devices accomplishing various applications are becoming an integral part of our daily life undoubtedly. Drones are characterized by extreme mobility, decent computing power, scalability, and a very short lifetime due to energy constraints. The rise of drones inevitably enabled swarms and drone networking applications. Drone networks is a path-breaking subclass of flying ad-hoc networks with unique capabilities and specific requirements. One very important challenge with swarms is the device authentication problem, in other words, proving the identity of a single or a group of drones that request to join the swarm. In this paper, we tackle this emerging problem and propose a novel context-aware mutual authentication protocol. The proposed protocol provides authentication for groups of drones and supports recovering a swarm in case of network separation. Likewise, the protocol can handle drone joins and leaves. Moreover, the protocol is not dependent on network infrastructure, secure storage, and secure channels. We tested the protocol using an automated formal security protocol verification tool, called Scyther. The tests resulted in the complete verification of the authentication and secrecy claims for arbitrary network instances and all defined use-cases. The protocol is also shown to have proven performance advantages over the existing schemes.

Wireless sensor networks (WSNs) achieving environmental sensing are fundamental communication layer technologies in the Internet of Things. Battery-powered sensor nodes may face many problems, such as battery drain and software problems. Therefore, the utilization of self-stabilization, which is one of the fault-tolerance techniques, brings the network back to its legitimate state when the topology is changed due to node leaves. In this technique, a scheduler decides on which nodes could execute their rules regarding spatial and temporal properties. A useful graph theoretical structure is the vertex cover that can be utilized in various WSN applications such as routing, clustering, replica placement and link monitoring. A capacitated vertex cover is the generalized version of the problem which restricts the number of edges covered by a vertex by applying a capacity constraint to limit the covered edge count. In this paper, we propose two self-stabilizing capacitated vertex cover algorithms for WSNs. To the best of our knowledge, these algorithms are the first attempts in this manner. The first algorithm is stabilized under an unfair distributed scheduler (that is, the scheduler which does not grant all enabled nodes to make their moves but guarantees the global progress of the system) at most O(n2) step, where n is the count of nodes. The second algorithm assumes 2-hop (degree 2) knowledge about the network and runs under the unfair scheduler, which subsumes the synchronous and distributed fair scheduler and stabilizes itself after O(n) moves in O(n) step, which is acceptable for most WSN setups. We theoretically analyze the algorithms to provide proof of correctness and their step complexities. Moreover, we provide simulation setups by applying IRIS sensor node parameters and compare our algorithms with their counterparts. The gathered measurements from the simulations revealed that the proposed algorithms are faster than their competitors, use less energy and offer better vertex cover solutions.

A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.

Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milic’s algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times.

A bridge is a critical edge whose fault disables the data delivery of a WSN component. Because of this, it is important to detect bridges and take preventions before they are corrupted. Since WSNs are battery powered, protocols running on WSN should be energy efficient. In this paper, we propose two distributed energy-efficient bridge detection algorithms for WSNs. The first algorithm is the improved version of Pritchard's algorithm where two phases are merged into a single phase and radio broadcast communication is used instead of unicast in order to remove a downcast operation and remove extra message headers. The second algorithm runs proposed rules on 2-hop neighborhoods of each node and tries to detect all bridges in a breadth-first search (BFS) execution session using O( N ) messages with O ( Δ ( log 2 ( N ) ) ) bits where N is the node count and Δ is the maximum node degree. Since BFS is a natural routing algorithm for WSNs, the second algorithm achieves both routing and bridge detections. If the second proposed algorithm is not able to to classify all edges within the BFS phase, improved version of Turau's algorithm is executed as the second phase. We show the operation of the algorithms, analyze them, and provide extensive simulation results on TOSSIM environment. We compare our proposed algorithms with the other bridge detection algorithms and show that our proposed algorithms provide less resource consumption. The energy saving of our algorithms is up to 4.3 times, while it takes less time in most of the situations.