Abstract Team formation in an environment where some relevant parameters are not known in advance is a challenging problem. Communicating automata and distributed algorithms have been used to describe protocols for team formation. A high‐level specification provides a mathematical description of a protocol or a program. TLA is a formal specification language designed to provide high‐level specifications of concurrent and distributed systems. The associated model checker known as TLC is capable of model checking the TLA specifications. Recently, formal specification of a team formation protocol is given using TLA when there is a single initiator (an agent or a robot) that initiates the team formation. Using TLA, we examine the formal specification for the multiple initiator situation and demonstrate that a composition technique can yield a single monolithic specification for the multiple initiator situation from the single initiator situation specification. We have used models of varying sizes, and the TLC model checker has confirmed that the protocol's specifications meet certain desired characteristics in each case.

With the rapid surge in cloud services, cloud load balancing has become a paramount research issue. The major part of a cloud computing system's operational costs is attributed to energy consumption. Therefore, to provide better QoS, considering the energy minimization factor in load balancing is essential. This paper addresses the latency and energy-aware load balancing problem in a cloud computing system. Specifically, two fundamental performance criteria–response time and energy–for the load balancing problem are considered. To solve this problem, first, the load balancing problem is formulated as an optimization problem. Then it is modeled as a cooperative game so that the solution of the game, called the Nash bargaining solution (NBS), can simultaneously optimize both criteria. The existence and computation of NBS are analyzed theoretically, and an efficient algorithm, called${{\sf L}}$atency and${{\sf E}}$nergy a${{\sf W}}$are load balancIng${{\sf S}}$cheme (${{\sf LEWIS}}$), is proposed to compute the NBS. Further, to assess the efficacy of${{\sf LEWIS}}$, it is compared with three other approaches, i.e.,${\mathsf {Coop\_{RT}}}$,${\mathsf {Coop\_{EN}}}$, and${\mathsf {NCG}}$, on problem instances of various settings. The experimental results show that${{\sf LEWIS}}$not only provides less response time while consuming less energy but also gauntness fairness to the end-users.

When a set of self-interested users shares multiple resources in a distributed system, we face the problem of allocating resources, called the load balancing problem. In particular, load balancing is defined as allocating the load to the servers of the distributed system such that jobs’ response time is minimized, and the utilization of servers is improved. In this article, the load balancing problem in a distributed system consists of a finite set of servers, and a finite set of users is studied. The load balancing problem considered here is a bi-objective problem with two highly probable conflicting objectives: (i) minimizing jobs’ response time (ii) providing the fair utilization of servers. In order to satisfy these two objectives simultaneously, both the objectives are considered in an integrated manner. Next, the load balancing problem is formulated as a noncooperative game; and to solve the game (i.e., to find the Nash equilibrium), a distributed load balancing algorithm (DLBA) is proposed. An experimental study is carried out to ascertain the efficacy of the proposed DLBA. Further, we compare DBLA with three existing load balancing approaches to evaluate its comparative effectiveness. The experimental results validate the effectiveness of the DLBA over the existing approaches.

Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals' wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users' devices probe requests and locations. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3 percent.

Multi-agent path planning (MAPP) is a challenging task that aims to find conflict free paths for all the agents in a given domain. Priority-based decoupled approach is one of the several approaches to solve a MAPP problem. It works as follows: first, find paths of individual agents and then restructure these paths based on some priority of the agents. Most of the existing decoupled approaches use centralised algorithms. However, multi-agent systems are inherently distributed, where agents have limited information and each agent may not know the total number of agents in the system. Some of these aspects are incorporated in DMAPP. DMAPP is an existing fully distributed algorithm that works in three phases: (i) individual path planning, (ii) priority decision-making and (iii) plan restructuring. However, DMAPP is incomplete, i.e. DMAPP may fail to find a solution, even if it exists. In this paper, we present a new distributed algorithm (DiMPP) which is complete. The computer simulations performed on some well-known benchmark domains reveal that DiMPP outperforms DMAPP in the number of problem instances solved. For larger problem instances, the time taken by DiMPP is orders of magnitude less than that of some existing centralised algorithms.

In context aware settings, if one or more services are unavailable, it may be useful to look for a set of services whose combined effect would be functionally equivalent to a given request. We show that the notion of functional equivalence is quite useful in some real world scenarios. In this paper, we suggest a method to obtain a composed service whose effect is functionally equivalent to the given request. The algorithm has been implemented on some domains and the results are promising.