The placement of mesh routers (MRs) in building a wireless mesh network (WMN) is the first step to ensure the desired network performance. Given a network domain, the fundamental issue in placing MRs is to find the minimal configuration of MRs so as to satisfy the network coverage, connectivity, and Internet traffic demand. In this paper, the problem is addressed under a constraint network model in which the traffic demand is non-uniformly distributed and the candidate positions for MRs are pre-decided. After formulating the MR placement problem, we first provide the theoretical analysis to validate the traffic demand and determine the optimal position of Internet gateway (IGW). To reduce complexity of determining the locations of MRs while satisfying the traffic constraint, we propose an effective heuristic algorithm to obtain an close-to-optimal solution. Finally, our simulation results verify our analytical model and show the effectiveness of our proposed algorithm.

Wireless mesh networks (WMNs) are evolving to be the key technology of the future. As the WMNs are envisioned to provide high bandwidth broadband service to a large community of users, the Internet gateway (IGW) which acts as a central point of Internet attachment for the mesh routers, it is likely to be a potential bottleneck because of its limited wireless link capacity. We propose a novel technique that elegantly balances the load among the different IGWs in a WMN. We switch the point of attachment of an active source serviced gateway depending on the average queue length at the IGW. The proposed load balancing scheme includes: an initial gateway discovery module, which determines a primary gateway for a mesh router and a load balancing module that rebalances the load among the gateways. We use ns-2 for evaluating our proposed scheme and we observe that the proposed scheme is able to balance the traffic efficiently

Wireless mesh networks are envisioned to support the wired backbone with a wireless backbone for providing Internet connectivity to residential areas and offices. Routing protocols designed for mobile ad hoc networks (MANETs) primarily concentrate on finding a single best possible route to any destination out of the various paths available. However in wireless mesh networks, traffic is primarily routed either towards the Internet gateways (IGWs) or from the IGWs to the access points (APs). Thus, if multiple APs choose the best throughput path towards a gateway, the traffic loads on certain paths and mesh routers increases tremendously thereby deteriorating the overall performance of the network. To this end, we propose a novel multi-path hybrid routing protocol, multipath mesh (MMESH), that effectively discovers multiple paths. We also propose elegant traffic splitting algorithms for balancing traffic over these multiple paths to synergistically improve the overall performance. Through extensive simulations, we observe that our protocol works very well to cope with the variations in the network. Our protocol also improves the performance of flows traversing multiple hops

In a wireless mesh network (WMN), mesh routers (MRs) are interconnected by wireless links and form a wireless backbone to provide ubiquitous high-speed Internet connectivity for mobile clients. The wireless backbone is tightly integrated with the Internet by nodes called as Internet gateways (IGWs). In this paper, we address the IGW deployment problem which is shown to be NP-hard. We first formulate it as a linear program (LP) issue, then develop two heuristic algorithms for the purpose of cost-efficient IGW deployment. We further compare our algorithms with a leading approach by extensive simulations. Our analysis shows the effectiveness of the proposed algorithms.

With the emergence of Internet of Things (IoT), the research on Smart Cities with wireless sensor networks (WSNs) got leveraged due to similarities between objectives in both Smart City and IoT. Along with them, research in controlling WSN faces new challenges and opportunities for data aggregation and routing has received consistent focus from researchers. Yet new techniques are being proposed to address modern challenges in WSN and efficient resource utilization. Moreover, solutions are required to integrate existing deployed WSN with ever increasing numbers of IoT devices in Smart Cities, that benefit both. In this work, we present an approach for routing in a WSN, in which IoT is used opportunistically to reduce the communication overhead of the sensors. In our approach, WSN deployed in a Smart City interacts with the IoT devices to route the data to the sink. We build a prototype Integration Platform for the WSN that allows interaction with IoT devices and utilizes them opportunistically that results in an energy efficient routing of data. Simulation results show that the direction is quite promising and our approach offers to utilize IoT to gain unique advantages.

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.