Wireless sensor networks (WSNs) have emerged to address unique challenges in urban environments. This survey dives into the challenges faced in urban areas and explores how WSN applications can help overcome these obstacles. The diverse applications of WSNs in urban settings discussed in this paper include gas monitoring, traffic optimization, healthcare, disaster response, and security surveillance. The innovative research is considered in an urban environment, where WSNs such as energy efficiency, throughput, and scalability are deployed. Every application scenario is distinct and examined in details within this paper. In particular, smart cities represent a major domain where WSNs are increasingly integrated to enhance urban living through intelligent infrastructure. This paper emphasizes how WSNs are pivotal in realizing smart cities by enabling real-time data collection, analysis, and communication among interconnected systems. Applications such as smart transportation systems, automated waste management, smart grids, and environmental monitoring are discussed as key components of smart city ecosystems. The synergy between WSNs and smart city technologies highlights the potential to significantly improve the quality of life, resource management, and operational efficiency in modern cities. This survey specifies existing work objectives with results and limitations. The aim is to develop a methodology for evaluating the quality of performance analysis. Various performance metrics are discussed in existing research to determine the influence of real-time applications on energy consumption, network lifetime, end-to-end delay, efficiency, routing overhead, throughput, computation cost, computational overhead, reliability, loss rate, and execution time. The observed outcomes are that the proposed method achieves a higher 16% accuracy, 36% network lifetime, 20% efficiency, and 42% throughput. Additionally, the proposed method obtains 36%, 30%, 46%, 35%, and 32% reduction in energy consumption, computation cost, execution time, error rate, and computational overhead, respectively, compared to conventional methods.

There has been a growing interest in defining models of automata enriched with time, such as finite automata extended with clocks (timed automata). In this paper, we study deterministic timed finite state machines (TFSMs), i.e., finite state machines with a single clock, timed guards and timeouts which transduce timed input words into timed output words. We solve the problem of equivalence checking by defining a bisimulation from timed FSMs to untimed ones and vice versa. Moreover, we apply these bisimulation relations to build the intersection of two timed finite state machines by untiming them, intersecting them and transforming back to the timed intersection. It is known that many problems like inclusion and equivalence checking are undecidable for timed automata. Our results show that TFSMs correspond to a decidable subclass of timed automata that admits a restricted form of $$\varepsilon $$ε -transitions (i.e., timeouts) where most of the relevant problems like equivalence and intersection are decidable.

Cloud service providers consistently leverage their computing infrastructures by adding reconfigurable hardware platforms such as field-programmable gate arrays (FPGAs) to their existing infrastructures. Adding FPGAs to a cloud environment involves non-trivial challenges. The first challenge is virtualizing FPGAs as part of the cloud resources. As a standard virtualization framework is lacking, there is a need for an efficient framework for virtualizing FPGAs. Furthermore, FPGA resources are used in conjunction with central processing units (CPUs) and graphics processing units (GPUs) to accelerate the execution of tasks. Therefore, to gain the benefits of these powerful accelerating platforms, the second challenge is to optimize the allocation of tasks into the capable resources within a cloud data center. This work proposes an FPGA virtualization framework that abstracts the physical FPGAs into virtual pools of FPGA resources. The work further presents an integer linear programming (ILP) optimization model to minimize the makespan of tasks where FPGA resources are part of the cloud data center. Given the complex nature of the problem, a simulated annealing (SA) metaheuristic is developed to achieve gains in performance compared to the exact method and to scale up and handle many tasks and resources while providing near-optimal solutions. Experimental results show that SA has reduced the makespan of a large dataset with 1000 tasks and 100 resources by up to 30% when compared to first-come-first-served (FCFS) and shortest-deadline-first (SDF) algorithms. Lastly, to quantify the performance of FPGA-enabled cloud datacenters, the work extends the CloudSim simulator (an open-source cloud simulator) to enable FPGA as a resource in its environment. The proposed virtualization framework and the SA scheduler are integrated into the environment. Simulation results show that the execution time of tasks is reduced by up to 78% when FPGA accelerators are used.