Containerized Edge computing offers lightweight, reliable, and quick solutions to latency-critical Machine Learning (ML) and Deep Learning (DL) applications. Existing solutions considering multiple Quality of Service (QoS) parameters either overlook the intricate relation of QoS parameters or pose significant scheduling overheads. Furthermore, reactive decisionmaking can damage Edge servers at peak load, incurring escalated costs and wasted computations. Resource provisioning, scheduling, and ML model selection substantially influence energy consumption, user-perceived accuracy, and delayoriented Service Level Agreement (SLA) violations. Addressing contrasting objectives and QoS simultaneously while avoiding server faults is highly challenging in the exposed heterogeneous and resource-constrained Edge continuum. In this work, we propose the EdgeAIBus framework that offers a novel joint container management and ML model selection algorithm based on Importance Weighted Actor-Learner Architecture to optimize energy, accuracy, SLA violations, and avoid server faults. Firstly, Patch Time Series Transformer (PatchTST) is utilized for CPU usage predictions of Edge servers for its 8.51% Root Mean Squared Error and 5.62% Mean Absolute Error. Leveraging pipelined predictions, EdgeAIBus conducts consolidation, resource oversubscription, and ML/DL model switching with possible migrations to conserve energy, maximize utilization and user-perceived accuracy, and reduce SLA violations. Simulation results show EdgeAIBus oversubscribed 110% cluster-wide CPU with real usage up to 70%, conserved 14 CPU cores, incurred less than 1% SLA violations with 2.54% drop in inference accuracy against industry-led Model Switching Balanced load and Google Kubernetes Optimized schedulers. Google Kubernetes Engine experiments demonstrate 80% oversubscription, 14 CPU cores conservation, 1% SLA violations, and 3.81% accuracy loss against the counterparts. Finally, constrained setting experiment analysis shows that PatchTST and EdgeAIBus can produce decisions within 100ms in a 1-core and 1 GB memory device.

Fog computing (FC) has emerged as a complementary solution to the centralized cloud infrastructure. An FC node is available in closer proximity to users and extends cloud services to the edge of the network in a highly distributed manner. However, with an increase in streaming and delay-sensitive Internet-of-Things (IoT) applications, FC also needs to address the issue of higher latency while forwarding compute-intensive jobs to remote cloud data centers. Hence, there is a need to investigate the use of computational resources at the edge of the network. Volunteer computing (VC) offers a reduction in the cost of maintaining high-performance computing by making use of user-owned underutilized or idle resources, e.g., laptops and desktop computers closer to fog devices. We propose volunteer-supported FC (VSFC), as a computing paradigm, that explores the interplay of these two distributed computing domains to help minimize inherent communication delays of cloud computing, energy consumption, and network usage. To this effect, we have extended the iFogSim toolkit to support VSFC. Extensive simulations show that VSFC outperforms traditional FC-cloud computing by reducing delay by 47.5%, energy by 93%, and network usage by 92% under normal to heavy load conditions.

Underwater wireless sensor networks (UWSNs) arouse as a better alternative of underwater wired instruments for data gathering. Acoustic signals offer low bandwidth and UWSNs faces low reliability, high delay and high energy consumption issues. Moreover, energy holes creation decreases network performance in terms of energy and throughput. The design of routing protocols which considers these challenges can improve data gathering. In this paper, we propose forward layered multipath power control-one (FLMPC-One) and FLMPC-Two routing protocols to reduce energy utilization, achieve reliability and elude energy holes. Both FLMPC-One and FLMPC-Two are multicast routing protocols. In order to achieve reliability, both schemes direct multiple copies towards surface through different paths which posses low noises by establishing binary tree. Mostly, current forwarder takes decision of next forwarder selection and gets deceived by energy holes. Therefore, FLMPC-One and FLMPC-Two makes decision by including two and three hops neighbors, respectively to detect and elude energy hole. In this way, they conserve energy and reduce delay introduced by retransmission.

In this paper, we propose an algorithm to alleviate coverage hole problem using geographic routing strategy for wireless sensor networks (WSNs). In order to accomplish desired results, an optimal number of forwarder nodes is computed along with the selection of path that has minimum energy consumption. Moreover, at each hop residual energy of a sensor is calculated and knowledge up-to one hop neighbors of forwarder node that ensures the avoidance of energy hole problem. Simulations are conducted to validate that our claim of outperforming compared existing schemes in terms of packet delivery ratio (PDR) and energy dissipation of the network nodes.