EdgeSynapse is a mechanism for energy-efficient sensing in remote, energy-constrained settings. It leverages neuromorphic principles by having sensor nodes and cluster heads generate discrete spikes when local integrator states cross thresholds. Specifically, each sensor uses a leaky integrate-and-fire (LIF) model to accumulate observations. When the membrane potential exceeds a threshold, the node emits an excitatory spike via LoRaWAN uplink and resets. Cluster heads similarly integrate incoming spikes and forward a higher-level spike when their threshold is reached, optionally using LoRaWAN downlink slots for inhibitory spikes to moderate traffic. This hierarchical spiking approach mimics biological signaling and reduces unnecessary transmissions. We provide a mathematical model of the LIF integration at sensors and cluster heads, along with the transmission algorithm tailored to LoRaWAN Class A communication. A preliminary simulation using real sensor data reveals favorable energy-accuracy trade-offs and a significant reduction in transmissions relative to send-on-Delta, alongside improved stability under synchronized bursts.

Smart environmental monitoring is crucial for public health and ecological balance as it enables us to monitor and react to environmental hazards. However, effective environmental monitoring can be hindered by the lack of infrastructure and high monetary costs. These challenges are even more pronounced in remote areas, where networking and energy sources are often limited or nonexistent. To address these challenges, we utilize UAVs to form a FANET which can provide effective communication infrastructure suitable for environment monitoring. Moreover, we utilize Edge Intelligence at these UAVs to increase the processing speed and reduce the data size that needs to be transmitted. Our results show that, compared to statically placed gateways, our solution is able to attain similar average age of information for monitoring results while also significantly increasing system capacity.

Federated Learning (FL) involves training models over a set of geographically distributed users. We address the problem where a single global model is not enough to meet the needs of geographically distributed heterogeneous clients. This setup captures settings where different groups of users have their own objectives however, users based on geographical location or task similarity, can be grouped together and by inter-cluster knowledge they can leverage the strength in numbers and better generalization in order to perform more efficient FL. We introduce a Hierarchical Multi-Cluster Computing Continuum for Federated Learning Personalization (HC3FL) to cluster similar clients and train one edge model per cluster. HC3FL incorporates federated transfer learning to enhance the performance of edge models by leveraging a global model that captures collective knowledge from all edge models. Furthermore, we introduce dynamic clustering based on task similarity to handle client drift and to dynamically recluster mobile (non-stationary) clients. We evaluate the HC3FL approach through extensive experiments on real-world datasets. The results demonstrate that our approach effectively improves the performance of edge models compared to traditional FL approaches.

The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information-and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.

The emerging field of edge computing is suffering from a lack of representative data to evaluate rapidly introduced new algorithms or techniques. That is a critical issue as this complex paradigm has numerous different use cases which translate into a highly diverse set of workload types.In this work, within the context of the edge computing activity of SPEC RG Cloud, we continue working towards an edge benchmark by defining high-level workload classes as well as collecting and analyzing traces for three real-world edge applications, which, according to the existing literature, are the representatives of those classes. Moreover, we propose a practical and generic methodology for workload definition and gathering. The traces and gathering tool are provided open-source.In the analysis of the collected workloads, we detect discrepancies between the literature and the traces obtained, thus highlighting the need for a continuing effort into gathering and providing data from real applications, which can be done using the proposed trace gathering methodology. Additionally, we discuss various insights and future directions that rise to the surface through our analysis.

Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid. Although there have been many works on MG energy management, most studies assume a perfect communication environment, where communication failures are not considered. In this article, we consider the MG as a multiagent environment with IoT devices in which AI agents exchange information with their peers for collaboration. However, the collaboration information may be lost due to communication failures or packet loss. Such events may affect the operation of the whole MG. To this end, we propose a multiagent Bayesian deep reinforcement learning (BA-DRL) method for MG energy management under communication failures. We first define a multiagent partially observable Markov decision process (MA-POMDP) to describe agents under communication failures, in which each agent can update its beliefs on the actions of its peers. Then, we apply a double deep$Q$-learning (DDQN) architecture for$Q$-value estimation in BA-DRL, and propose a belief-based correlated equilibrium for the joint-action selection of multiagent BA-DRL. Finally, the simulation results show that BA-DRL is robust to both power supply uncertainty and communication failure uncertainty. BA-DRL has 4.1% and 10.3% higher reward than Nash deep$Q$-learning (Nash-DQN) and alternating direction method of multipliers (ADMM), respectively, under 1% communication failure probability.

Wireless sensor networks have wide applications in monitoring applications. However, sensors’ energy and processing power constraints, as well as the limited network bandwidth, constitute significant obstacles to near-real-time requirements of modern IoT applications. Offloading sensor data on an edge computing infrastructure instead of in-cloud or in-network processing is a promising solution to these issues. Nevertheless, due to geographical dispersion, ad-hoc deployment, and rudimentary support systems compared to cloud data centers, reliability is a critical issue. This forces edge service providers to deploy a huge amount of edge nodes over an urban area, with catastrophic effects on environmental sustainability. In this work, we propose ARES, a two-stage optimization algorithm for sustainable and reliable deployment of edge nodes in an urban area. Initially, ARES applies multi-objective optimization to identify a set of Pareto-optimal solutions for transmission time and energy; then it augments these candidates in the second stage to identify a solution that guarantees the desired level of reliability using a dynamic Bayesian network based reliability model. ARES is evaluated through simulations using data from the urban area of Vienna. Results demonstrate that it can achieve a better trade-off between transmission time, energy-efficiency, and reliability than the state-of-the-art solutions.

Edge computing services are exposed to infrastructural failures due to geographical dispersion, ad hoc deployment, and rudimentary support systems. Two unique characteristics of the edge computing paradigm necessitate a novel failure resilience approach. First, edge servers, contrary to cloud counterparts with reliable data center networks, are typically connected via ad hoc networks. Thus, link failures need more attention to ensure truly resilient services. Second, network delay is a critical factor for the deployment of edge computing services. This restricts replication decisions to geographical proximity and necessitates joint consideration of delay and resilience. In this article, we propose a novel machine learning based mechanism that evaluates the failure resilience of a service deployed redundantly on the edge infrastructure. Our approach learns the spatiotemporal dependencies between edge server failures and combines them with the topological information to incorporate link failures. Ultimately, we infer the probability that a certain set of servers fails or disconnects concurrently during service runtime. Furthermore, we introduce Dependency- and Topology-aware Failure Resilience (DTFR), a two-stage scheduler that minimizes either failure probability or redundancy cost, while maintaining low network delay. Extensive evaluation with various real-world failure traces and workload configurations demonstrate superior performance in terms of availability, number of failures, network delay, and cost with respect to the state-of-the-art schedulers.

Blockchain-based decentralized multi-cloud has the potential to reduce cloud infrastructure costs and to enable geographically distributed providers of any size to monetize their computational resources. In this context, guarantees that the computational results are delivered within the promised time and budget must be provided despite the limited information available about the location and ownership of resources. Providers might claim to execute the services to get compensated for the computation even though returning incomplete or incorrect results. In this paper, we define a model to predict provider reliability, that is, the probability of failure-free execution of computational tasks and correctness of the computed outputs, by extracting the potential dependencies between providers from historical log traces. This model can then be utilized in the definition of provider reputation or the scheduling of new services. Indeed, we propose a probabilistic scheduler that chooses the providers that meet the reliability constraints among others. Finally, we validate the proposed solutions with real traces from a decentralized cloud provider and hint at the benefits of predicting reliability in this context.

Abstract Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.

As the devices that make up the Internet become more powerful, algorithms that orchestrate cloud systems are on the verge of putting more responsibility for computation and storage on these devices. In our current age of Big Data, dissemination and storage of data across end cloud devices is becoming a prominent problem subject to this expansion. In this paper, we propose a distributed data dissemination approach that relies on dynamic creation/replacement/removal of replicas guided by continuous monitoring of data requests coming from edge nodes of the underlying network. Our algorithm exploits geographical locality of data during the dissemination process due to the plenitude of common data requests that stem from the clients within a close proximity. Our results using both real-world and synthetic data demonstrate that a decentralized replica placement approach provides significant cost benefits compared to client side caching that is widely used in traditional distributed systems.

Federated clouds and cloud brokering allow migration of virtual machines across clouds and even deployment of cooperating VMs in different cloud data centers. In order to fully benefit from these new opportunities, we propose a heuristic that outputs a matching between virtual machine and cloud data centers by taking resource capacities, VM topologies, performance and resource costs into account. Results of our initial evaluation using the CloudSim Framework indicate that, proposed heuristic is promising for a better optimized placement of networked VM groups onto the federated cloud topology.

Application placement is an important concept when providing software as a service in cloud environments. Because of the potential downtime cost of application migration, most of the time additional resource acquisition is preferred over migrating the applications residing in the virtual machines (VMs). This situation results in under-utilized resources. To overcome this problem static/dynamic estimations on the resource requirements of VMs and/or applications can be performed. A simpler strategy is using heuristics during application placement process instead of naively applying greedy strategies like round-robin. In this paper, we propose a number of novel heuristics and compare them with round robin placement strategy and a few proposed placement heuristics in the literature to explore the performance of heuristics in application placement problem. Our focus is to better utilize the resources offered by the cloud environment and at the same time minimize the number of application migrations. Our results indicate that an application heuristic that relies on the difference between the maximum and minimum utilization rates of the resources not only outperforms other application placement approaches but also significantly improves the conventional approaches present in the literature.