In a mobile ad hoc network (MANET), all nodes are communicated with one another across wireless networks to create a temporary network without the support of centralised management. Due to dynamic topology in MANET, secure routing is a crucial issue. The existing secure routing protocol and their security concerns are analysed in this work. The suggested Swarm Intelligence‐based Secure Ad‐hoc On‐demand Distance Vector (SIS‐AODV) algorithm offers security by applying a secret key and hash mechanism to prevent the involvement of malicious nodes in routing operations. A secure routing system of MANET guards against internal and external network attacks. The proposed SIS‐AODV algorithm consists of two sections: the secret key value generated by applying Elliptical Curve Cryptography (ECC)‐based algorithm and the PRESENT algorithm to encrypt the data packets. Besides, authentication and non‐repudiation are applied using the H‐PRESENT 128 algorithm. The PRESENT algorithm and H‐PRESENT 128 hash function require less computational power. Centralised management is optional in this scheme, so overhead decreases. The second section of SIS‐AODV consists of Ant Colony Grey Wolf Optimization over the AODV algorithm to improve network performance while implementing a security algorithm over MANET. Analysis results show maximum performance with a packet delivery ratio of 98% and throughput of 85%. In addition, end‐to‐end delay is reduced by up to 25%, and routing overhead decreases by up to 20%. Keywords: AODV, Elliptical Curve, PRESENT, H‐PRESENT, Euclidean Algorithm, ACO, GWO, Blackhole attack.

SIS‐AODV consist of Ant Colony Grey Wolf Optimisation (ACGWO) over AODV algorithm to improve network performance while implementing security algorithm over MANET. Analysis results shows maximum performance with the PDR of 98% and throughput of 85%. In addition, end‐to‐end delay is reduced up‐to 25% and routing overhead decrease up‐to 20%.image

Extensive use of the Internet of Things (IoT) in smart homes makes users' lives easy and comfortable. Yet, these resource‐constrained devices are prone to manifold security attacks. The sinkhole attack is one of the most destructive attacks that disrupt smart home operations, causing user dissatisfaction. Existing intrusion detection systems (IDS) cannot handle sinkhole attacks competently as they (i) do not consider the node capacity for being an IDS agent, leading to a low attack detection ratio, (ii) do not examine the sinkhole node's role when mitigating attacks, causing remaining network disconnection with the root node and (iii) do not consider replacing energy‐exhausted IDS nodes, causing connectivity loss of partial network with the root. This paper addresses these shortcomings and adequately presents a mechanism to handle sinkhole attacks. A formulation for assigning weights to network nodes based on their resources is proposed here. An IDS placement strategy is introduced to place IDS agents on particular resourceful nodes that extend network lifetime and enhance attack detection capability. We present a novel attack detection and mitigation strategy by ensuring network connectivity. The proposed mechanism achieves 95% attack detection accuracy and reduces false negative rates by 25% and energy consumption reasonably compared to the state‐of‐the‐art.

A mechanism to effectively handle sinkhole attacks in smart homes is presented, addressing existing IDS systems' shortcomings. A formulation for assigning weights to network nodes based on their resources is proposed and an IDS placement strategy to place IDS agents on particular resourceful nodes that extend network lifetime and enhance attack detection capability is introduced. Besides, a novel attack detection and mitigation strategy that ensures network connectivity is presented. The proposed mechanism achieves 95% attack detection accuracy, reduces false negative rates by 25% and consumes energy reasonably compared to the state‐of‐the‐art.image

Internet of Things is an emerging paradigm based on interconnecting physical and virtual objects with each other and to the Internet. Most connected things fall into the category of constrained devices, with restricted resources (processing power, memory, and energy). These low‐power and lossy networks (LLNs) are known for their instability, high loss rates and low data rates, which makes routing one of the most challenging problems in low‐cost communications. A routing protocol for low‐power and lossy networks (RPL) is a proactive dynamic routing protocol based on IPv6. This protocol defines an objective function (OF) that utilises a set of metrics to select the best possible path to the destination. Minimum rank hysteresis objective function (MRHOF) and objective function zero (OF0) are the most basic OFs, where the first one selects the path to the sink based on the expected transmission count (ETX) metric, and OF0 is based on the hop count (HC). These two metrics prioritise either brute performance (i.e. ETX) or simplicity (i.e. HC). Therefore, using a single metric with an OF can either limit the performance or have an inefficient impact on load management and energy consumption. To overcome these challenges, a routing metric based on MRHOF OF which takes into consideration the link‐based routing metric (i.e. ETX) and node‐based metric (i.e. remaining energy) for route selection is provided. Expected transmission count remaining energy (ETXRE) is evaluated through 36 scenarios with different parameters. Preliminary results show that ETXRE outperforms ETX and RE in terms of end‐to‐end delay by an average of at least 17%, packet delay by 13% and consumes 10% less energy.

Expected transmission count remaining energy (ETXRE) is a new routing metric based on the combination of expected transmission count and the remaining energy routing metrics. ETXRE shows a great potential to save up on the consumed energy and lowering the delay, and gives exceptionally competitive results in the other areas (i.e. packet delivery ratio, convergence time and throughput).image

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks.

Congestion Control in constrained IoT Networks is becoming a pressing issue, given the steep rise in IoT networks and devices. On the other hand, transmitting data from Internet‐constrained IoT devices effectively over the Internet using legacy network architecture, protocols, and communication technologies is challenging for such resource‐limited devices. This paper proposes a practical, light‐weight congestion control mechanism for IoT networks, making them scalable in data rates and device count.image