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In the current paper, a generalized Montgomery identity is obtained with the help of Taylor’s formula on time scales. The obtained identity is used to establish Ostrowski inequality, mid-point inequality, and trapezoid inequality. Moreover, the weighted versions of generalized Montgomery identity and respective Ostrowski inequality are also discussed. Special cases are obtained for different time scales to obtain new and existing results.

Nowadays, both the modern and developing countries are planning to deploy Internet of Vehicles for smart transportation systems to face the traffic congestion problems. Shifting from current road conditions to smart ones is a real challenge where all the vehicles and road infrastructure are not equipped with wireless network interfaces. The urban environment is more congested than highway scenarios and has more issues of delay and travel time. Various types of route suggestion protocols have been proposed where most of them consider smart vehicles which are equipped with smart and intelligent communication devices. The most of existing schemes have not considered non-equipped vehicles which have a major cause of traffic congestion. This paper presents a route suggestion protocol to suggest an optimal congestion aware route in the network. Equipped and non-equipped vehicles are taking into account for congestion index formulation along with driving distraction factors. Simulation results indicated the greater performance of proposed protocol and reduce the travel time effectively when works with IoV as compared to traditional route suggestion protocols.

In the past few decades, research has been carried out to automatically find humans in a video sequence. Automatically detecting humans in videos is gaining interest for numerous applications such as driver assistance system, security, people counting, human gait characterization, video annotations, retrieval, or crowd flow analysis. Manual annotation of a video is a time-consuming task that involves human annotators which varying biases. In this paper, we have presented three computer vision algorithms (contour-based, HOG-based and SURF-based) and proposed a deep learning technique that automatically extracts spatiotemporal annotations of human and represents it by a bounding box. We have performed experiments and the accuracy obtained for each method is 86%, 92.5%, 94%, and 95.5%, respectively. Results show that not only annotation accuracy has increased but the human effort has reduced with respect to manual annotations. We have also introduced a new dataset ASSVS_KICS which is captured through a high-quality stationary camera and contain scenarios based on our community for video surveillance research.

Wireless networking is experiencing a tremendous growth in new standards of communication and computer applications. Currently, wireless networks exist in various forms, providing different facilities. However, due to some limitations as compared with wired counterparts, wireless networks face several major challenges and one of them is optimum bandwidth allocation. The focus of optimum bandwidth allocation is to reduce the losses and satisfying quality of service (QoS) requirements. In wireless networks, the term bandwidth allocation is attributed as the distribution of bandwidth resources among different users, which affects the serviceability of the entire system. Though many studies related to bandwidth allocation have been reported already, however, only sub-optimal solutions have been provided so far. In this research, we proposed to use the differential evolution (DE) algorithm to allocate bandwidth through a bandwidth reservation scheme in the Cellular IP network, in order to improve the QoS at an acceptable level. DE belongs to a class of evolutionary algorithms (EA), like particle swarm optimization and genetic algorithm. A DE-based method is used which looks for any free bandwidth in the cell or in adjacent cells and provides it to the cell where required. In case, it fails to find the free available bandwidth then it will search the bandwidth which is standby for non real-time users and allocates it to the real-time users that will help in improving the QoS in terms of connection/call dropping probability for real-time users. Simulation results show that the proposed method performs better as compared to previously used EA models for bandwidth allocation.

A Wireless Mesh Network (WMN) can provide Internet connectivity to end users through heterogenous access network technologies. However, the mobility of mobile nodes across these access networks in WMNs results in service disruption. Existing mobility management protocols are designed for single hop networks and are centralized in nature. A Distributed IP-based Mobility Management Protocol (DIMMP) is proposed in this paper that provides seamless mobility with service continuation for mobile nodes when they roam across WMNs. Instead of relying on a centralized mobility anchor, the mobility functionality is distributed at multiple nodes in the WMN, in order to reduce the chances of potential single point of failure. The proposed protocol manages both types of mobilities i.e. intra-WMN and inter-WMN and uses a new enhanced route optimization procedure. Simulation results show that this work has contributed by improving the performance of handoff procedure with respect to handoff latency, packet loss and signalling overhead, as compared to the existing protocols.

In the paper, some variants of Montgomery identity with the help of delta and nabla integrals are established which are useful to produce Montgomery identity involving alpha diamond integrals for function of two variables. The aforementioned identity is discussed in discrete, continuous, quantum calculus as well and employed to obtain Ostrowski type inequality for monotonically increasing function with respect to both parameters.