This article introduces an enhanced version of previously developed self-optimizing algorithm that controls the handover (HO) parameters of a long-term evolution base station in order to diminish and prevent the negative effects that can be introduced by HO (radio link failures, HO failures and ping-pong HOs) and thus improve the overall network performance. The default algorithm selects the best hysteresis and time-to-trigger combination based on the current network status. The enhancement proposed here aims to maximize the gain provided by the algorithm by improving its convergence time. The effects of this enhancement have been studied in a rural scenario setting and compared to the original algorithm; the results show a clear improvement, faster convergence, and better network performance, because of the enhancement.

The advent of Internet-of-Things (IoT) applications, such as environmental monitoring, smart cities, and home automation, has taken the IoT concept from hype to reality at a massive scale. However, more mission-critical application areas such as energy, security and health care do not only demand low-power connectivity, but also highly reliable and guaranteed performance. While fulfilling these requirements under controlled conditions such as urban and indoor environments is relatively trivial, tackling the same obstacles in a more challenging and dynamic setting is significantly more complicated. In environments where infrastructure is sparse, such as rural or remote areas, specialized infrastructure-less ad-hoc solutions are needed, which provide long-range multi-hop connectivity to remote sensors and actuators. In this paper we propose a new general-purpose IoT platform based on a combination of Low-power Wireless Personal Area Network (LoWPAN) and multi-hop Wireless Sensor Network (WSN) technology. It supports reliable and guaranteed realtime data dissemination and analysis, as well as actuator control, in dynamic and challenging infrastructure-less environments. In this paper, we present the IoT platform architecture and an initial hard-and software prototype. Moreover, a use case based on realtime monitoring and training adaptation for cyclists is presented. Based on this case study, evaluation results are presented that show the ability of the proposed platform to operate under challenging and dynamic conditions.

The diversity of multimedia-enabled devices supporting streamed multimedia is ever growing. Multicast delivery of TV channels in IP networks to a heterogeneous set of clients can be organised in many different ways, which brings up the discussion which one is optimal. Scalable video streaming has been believed to be more efficient in terms of network capacity utilisation than simulcast video delivery because one flow can serve all terminals, while with simulcast all resolutions are offered in parallel. At the same time, it is also largely recognised that in order to provide the same video quality compared to non-layered video coding, scalable video coding (SVC) incurs a bit rate penalty.In this paper we compare simulcast and SVC in terms of their required capacity in an IPTV network scenario where a bouquet of TV channels is offered to the subscribers. We develop methods to calculate and approximate the capacity demand for two different subscriber behaviour models. These methods are then used to explore the influence of various parameters: the SVC bit rate penalty, the number of offered channels, the channel popularity and the number of subscribers. The main contribution of this paper is that we derive an analytical formula to calculate the SVC limit bit rate penalty beyond which SVC is less efficient than simulcast. In the realistic IPTV examples considered here, the limit is found to lie between 16% and 20%, while the reported values for this coding penalty range from 10% up to 30% for current H.264 SVC codecs, indicating that SVC in IPTV is not always more efficient than simulcast.

Abstract. In this paper the D-BMAP /G/1 queue is considered. The goal is to derive an explicit expression for the transform of the queueing delay of the nth arriving customer, based on a transient analysis. While deriving this transform, intermediate results such as an explicit expression for the transform of the probability of having an empty system at the nth departure, are also obtained. These results are then applied to the dimensioning of a playout buffer for variable bit rate video traffic. lntroductionIn this paper the D-BMAP /G/1 queue is considered. This is a discrete-time expression for the transform of the probability of having an empty system at the nth departure, are also obtained. The transform of the queueing delay of the nth arrival is used to dimension a playout buffer for a video application. The time the video application needs to keep the first packet of a video stream in the buffer before starting to playout is determined such that underflow is avoided.The structure of the paper is as follows. Section 2 introduces the D-BMAP arrival process as well as the queueing model considered in this paper. It also summarizes the transient analysis of the queueing system and presents an expression for the transform ofthe queueing delay ofthe nth arrival in the D-MAP /G/1 *

The Industrial Internet of Things is a challenge for wireless sensor networks, where ultrareliability, guaranteed performance, and ultralow‐power consumption are mandatory requirements to enable critical IoT applications. The time‐slotted channel hopping (TSCH) mode of the IEEE 802.15.4e standard is one of the most promising technologies to accomplish these requirements by yielding guaranteed performance and, simultaneously, efficiently combating external interference and multipath fading. One of the challenges in TSCH networks is to build an efficient schedule for managing the access of the nodes to the timeslots and channels. Several scheduling algorithms have been proposed. Currently, the Scheduling Function Zero SFx is one of the proposed scheduling algorithms for 6TiSCH, the ongoing standard for an IPv6‐enabled stack working over TSCH. SFx is based on random resource allocation according to the traffic demand, which makes it inadequate for large‐scale and dense deployments due to internal collisions. This paper has two main goals. First, we extensively investigate the performance of SFx for large‐scale and dense scenarios, analyze its scalability, and identify its scheduling limitations. Second, we present a new TSCH‐based scheduling function, ie, the distributed broadcast‐based scheduling (DeBraS) algorithm, a scheduling solution designed for dense deployments based on sharing scheduling information between nodes to reduce collisions proactively. We show, through extensive and large‐scale simulations, that DeBraS supports much larger densities than state‐of‐the‐art scheduling functions, outperforming the current distributed 6TiSCH algorithm SFx up to 1.61 times in terms of throughput for large network sizes, at the expense of an increase in power consumption.

Modelling the time-varying cell capacity in LTE networksBart Sas · Elena Bernal-Mor · Kathleen Spaey · Vicent Pla · Chris Blondia · Jorge Martinez-Bauset adaptive modulation and coding (AMC) is applied. With AMC, different modulation and coding schemes (MCSs) are used to serve different users in order to maximise the throughput and range. The used MCS depends on the quality of the radio link between the base station and the user. Data is sent towards users with a good radio link with a high MCS in order to utilise the radio resources more efficiently while a low MCS is used for users with a bad radio link. Using AMC however has an impact on the cell capacity as the quality of a radio link varies when users move around; this can even lead to situations where the cell capacity drops to a point where there are too little radio resources to serve all users. AMC and the resulting varying cell capacity notably has an influence on admission control (AC). AC is the algorithm that decides whether new sessions are allowed to a cell or not and bases its decisions on, amongst others, the cell capacity. The analytical model that is developed in this paper models a cell with varying capacity caused by user mobility using a continuous-time Markov chain (CTMC). The cell is divided into multiple zones, each corresponding to the area in which data is sent towards users using a certain MCS and transitions of users between these zones are considered. The accuracy of the analytical model is verified by comparing the results obtained with it to results obtained from simulations that model the user mobility more realistically. This comparison shows that the analytical model captures the varying cell capacity very accurately; only under extreme conditions differences between the results are noticed.The developed analytical and simulation models are then used to investigate the effects of a varying cell capacity on AC. The analytical and simulation models are also used to study an optimisation algorithm that adapts the parameter of the AC algorithm which deThe final publication is available at Springer via http://dx.doi.org/10.1007/s11235-013-9782-2 2 Bart Sas et al.termines the amount of resources that are reserved in order to mitigate the effects of the varying cell capacity. Updating the parameter of the AC algorithm is done by reacting to certain triggers that indicate good or bad performance and adapt the parameters of the AC algorithm accordingly. Results show that using this optimisation algorithm improves the quality of service (QoS) that is experienced by the users.

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The development of self-management solutions for (multi-technology, multi-layer) mobile communication networks is driven by their increasing operational complexity. Initial stand-alone SON (Self-Organizing Networks) solutions are already available, but are not sufficient to handle the networks of tomorrow. In this paper we present our approach at developing a unified management framework that integrates the existing and future advanced SON functions across several radio access technologies. The envisioned self-management system comprises (i) an integrated SON management system, in charge of policy transformation/supervision and conflict detection/handling; (ii) advanced multi-RAT/layer SON functions; and (iii) a Decision Support System providing measurement-based assistance for residual operational tasks, such as timely recommendations for targeted new site deployments.