Ubiquitous sensing and unique characteristics of wireless sensor networks (WSNs) have led to an increase in application areas such as smart parking, environmental monitoring, automotive industries and sports. In recent years, WSNs have gained more significance as the foundation infrastructure for the Internet of Things (IoT), which has greatly increased the number of connected objects with instantaneous communication and data processing. However, designing energy‐efficient models for integrating WSNs into IoT is a challenging issue due to scalability and interoperability of IoT, and previous approaches designed for WSNs cannot be applied directly. This study proposes two energy‐efficient models for WSNs in the IoT environment: (i) a service‐aware clustering model where individual sensor nodes are assigned roles based on their service delivery; and (ii) an energy‐aware clustering model. Performance evaluation shows better energy efficiency, end‐to‐end delay and network load balance of the proposed models for integrating wireless sensor networks into the IoT protocol compared with low‐energy adaptive clustering hierarchy centralised protocol and fuzzy C‐means clustering protocol.

Wireless sensor networks (WSNs) have been widely applied in many areas for real‐time event detection. They are designed using both mobile and static sensor nodes (SNs) for different applications such as smart parking, environmental monitoring, health care systems, automotive industries, sports, open space surveillance, and so on. WSNs communicate through wireless mediums and are accessible to anyone, which make SNs susceptible to different types of attacks. Distributed denial of service (DDoS) is one such attack. It wastes the limited energy of SNs and causes loss of data packets within a network. A DDoS attack launches a coordinated attack by flooding the target nodes with bogus requests, thus exhausting their resources, and forcing them to deny service to legitimate member nodes. In this study, the authors propose a message analyser scheme for WSNs. The method is capable of detecting compromised SNs vulnerable to a DDoS attack. In addition, it is able to detect all compromised messages transmitted by the attackers to the base station through the sender nodes. The proposed method is compared with other related protocols. The results show that their method can effectively detect and defend against DDoS attacks in WSNs.

The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO) is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP), which is considered to be an NP-hard Combinatorial Optimization Problem (COP). A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO) algorithm on TSP were compared to other meta-heuristic algorithms.