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The IEEE 802.15.4 standard is one of the most widely used link layer technology for building Internet of Things. It specifies several physical layer options and MAC layer for meeting low-power and low-rate requirements of devices deployed in a network of IoT. The standard also specifies a synchronization scheme for devices connected in a star topology, operating in beacon-enabled (BE) mode using periodic beacons. The BE mode facilitates synchronization among devices for data transmission and is suitable for large networks to establish low duty-cycles. Absence of a such a scheme for a cluster-tree network has confined its application only to non-beacon mode. The challenge here is to schedule beacon frame transmissions of multiple devices in a non-overlapping manner to avoid beacon collisions. This paper tackles the problem of synchronization by proposing localized beacon synchronization (LBS) scheme, a distributed technique for beacon scheduling in cluster-tree network topologies. LBS uses 2-hop information and association order to compute beacon transmission offsets that better utilize the available time slots, incur fewer transmissions, and is highly scalable. Further, we analytically show that the schedulability of the proposed scheme is higher compared to other related schemes. In addition, we also address the important issue of resynchronization that has been ignored in all of the prior works. The proposed re-synchronization mechanisms consider the interdependencies between synchronization and duty-cycling schemes and are shown to significantly lower the synchronization overhead when synchronization among devices is lost.

The IEEE 802.15.4 standard is one of the widely adopted networking specification for Internet of Things (IoT). It defines several physical layer (PHY) options and medium access control (MAC) sub-layer protocols for interconnection of constrained wireless devices. These devices are usually battery-powered and need to support requirements like low-power consumption and low-data rates. The standard has been revised twice to incorporate new PHY layers and improvements learned from implementations. Research in this direction has been primarily centered around improving the energy consumption of devices. Recently, to meet specific Quality-of-Service (QoS) requirements of different industrial applications, the IEEE 802.15.4e amendment was released that focuses on improving reliability, robustness and latency. In this paper, we carry out a performance-to-cost analysis of Deterministic and Synchronous Multi-channel Extension (DSME) and Time-slotted Channel Hopping (TSCH) MAC modes of IEEE 802.15.4e with 802.15.4 MAC protocol to analyze the trade-off of choosing a particular MAC mode over others. The parameters considered for performance are throughput and latency, and the cost is quantified in terms of energy. A Markov model has been developed for TSCH MAC mode to compare its energy costs with 802.15.4 MAC. Finally, we present the applicability of different MAC modes to different application scenarios.

With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-user's limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels. INDEX TERMS 5G and beyond, computation offloading, mobile edge computing, fog computing, resource allocation, offloading decision. I. INTRODUCTION With the emergence of ultra-reliable and low-latency communications (uRLLC) [1]-[4], the latency and reliability-aware mission-critical applications are increasingly growing up. To mention, a few examples are, autonomous driving, virtual and augmented reality, and cloud robotics, remote surgery, and factory automation. However, at the same time, the enduser's computational resources limit the user's experience (e.g., latency and reliability) for the computational-intensive applications. The cloud computing has already proven its significance to process the computational-intensive tasks, however, the physical distance between the end-user and The associate editor coordinating the review of this manuscript and approving it for publication was Christos Verikoukis. remote cloud data center and burden on fronthaul link are the major barrier for low-latency-aware applications. To address the above challenges, Fog computing [5], [6], often viewed as a middleware between end-user and cloud, extends the computational, communication, and storage resources of the cloud computing close to the network edge. A. MOTIVATION For computational-intensive task processing in a fog computing scenario, the end-user offloads the data either partially or entirely to the nearby fog computing node(s). It would be an ideal solution if a single fog computing node (hereinafter referred to as fog nodes) is able to com...

Wormhole attack is one of the most severe security threats in wireless mesh network that can disrupt majority of routing communications, when strategically placed. At the same time, most of the existing wormhole defence mechanisms are not secure against wormhole attacks that are launched in participation mode. In this paper, we propose WRSR, a wormhole-resistant secure routing algorithm that detects the presence of wormhole during route discovery process and quarantines it. Unlike other existing schemes that initiate wormhole detection process after observing packet loss, WRSR identifies route requests traversing a wormhole and prevents such routes from being established. WRSR uses unit disk graph model to determine the necessary and sufficient condition for identifying a wormhole-free path. The most attractive features of the WRSR include its ability to defend against all forms of wormhole (hidden and Byzantine) attacks without relying on any extra hardware like global positioning system, synchronized clocks or timing information, and computational intensive traditional cryptographic mechanisms.

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