Collaborative Detection and Prevention of Sybil Attacks against RPL-Based Internet of Things

The Internet of Things(IoT)comprises numerous resource-constrained devices that generate large volumes of data.The inherent vulnerabilities in IoT infrastructure,such as easily spoofed IP and MAC addresses,pose significant security challenges.Traditional routing protocols designed for wired or wireless networks may not be suitable for IoT networks due to their limitations.Therefore,the Routing Protocol for Low-Power and Lossy Networks(RPL)is widely used in IoT systems.However,the built-in security mechanism of RPL is inadequate in defending against sophisticated routing attacks,including Sybil attacks.To address these issues,this paper proposes a centralized and collaborative approach for securing RPL-based IoT against Sybil attacks.The proposed approach consists of detection and prevention algorithms based on the Random Password Generation and comparison methodology(RPG).The detection algorithm verifies the passwords of communicating nodes before comparing their keys and constant IDs,while the prevention algorithm utilizes a delivery delay ratio to restrict the participation of sensor nodes in communication.Through simulations,it is demonstrated that the proposed approach achieves better results compared to distributed defense mechanisms in terms of throughput,average delivery delay and detection rate.Moreover,the proposed countermeasure effectively mitigates brute-force and side-channel attacks in addition to Sybil attacks.The findings suggest that implementing the RPG-based detection and prevention algorithms can provide robust security for RPL-based IoT networks.

Adaptive Multi-Cost Routing Protocol to Enhance Lifetime for Wireless Body Area Network

Wireless Body Area Network(WBAN)technologies are emerging with extensive applications in several domains.Health is a fascinating domain of WBAN for smart monitoring of a patient’s condition.An important factor to consider in WBAN is a node’s lifetime.Improving the lifetime of nodes is critical to address many issues,such as utility and reliability.Existing routing protocols have addressed the energy conservation problem but considered only a few parameters,thus affecting their performance.Moreover,most of the existing schemes did not consider traffic prioritization which is critical in WBANs.In this paper,an adaptive multi-cost routing protocol is proposed with a multi-objective cost function considering minimum distance from sink,temperature of sensor nodes,priority of sensed data,and maximum residual energy on sensor nodes.The performance of the proposed protocol is compared with the existing schemes for the parameters:network lifetime,stability period,throughput,energy consumption,and path loss.It is evident from the obtained results that the proposed protocol improves network lifetime and stability period by 30%and 15%,respectively,as well as outperforms the existing protocols in terms of throughput,energy consumption,and path loss.

A V2I communication-based pipeline model for adaptive urban traffic light scheduling

Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%-78% and average stop frequency by 12%-34% in the context of traffic management. The GTLM reduces travel time by 22%^44% and 30%-55% under two types of traffic conditions and achieves optimal performance in congested scenarios.