Massive gatherings require hosting authorities to plan and deploy extensive arrangements to facilitate attendees while considering crowd safety the core objective. The task becomes more challenging when crowd is diverse in terms of language, culture, ethics and age-group. Hajj is an annual event where more than 2 million pilgrims from all over the world, gather in Makkah Al-Mukarramah to perform a series of spatio-temporal rituals. To plan and execute the event, the management needs to understand the behavior of both individuals and groups of pilgrims belonging to different nationalities. In this paper, we proposed a big data framework that collects mobile sensory data from all pilgrims by providing them location-based context-aware services through a mobile application. We utilize the collected data to study pedestrians' flow rate, mobility patterns for individuals and groups and their behaviour and preferences during movement. The extracted information can be analyzed to propose solutions to crowd related challenges, including mobility schedules, crowd modelling and simulation and emergency evacuation plans.

Turbulence in crowds due to random, unintentional and uncontrolled movement of the mass is a critical and deadly situation. Live concerts, football matches, festivals are examples of large crowd gatherings. One example of a regularly occurring highly crowded event is Hajj, where more than 3 million people gather in Makkah for five days every year to perform their rituals. Every individual performing Hajj wants to complete their rituals correctly by visiting specific places at specific dates and time. In the past, severe crowd stampedes and casualties have taken place, especially at the site of stoning the Jamarat. This paper presents an interactive tool for safe rescheduling of groups based on their preferences for performing the stoning ritual at Jamarat. The interactive visual tool allows end-users to handle the request of rescheduling on two parameters, safety and temporal preferences. The safety parameter considers the capacity of each road so that it will not cross the limit set by authorities, and the temporal preference parameter considers the time as per the Islamic school of Jurisprudence of the pilgrim. The tool can help schedule the movement of pilgrims incrementally and interactively. The tool was used successfully in Hajj'19. Furthermore, the tool provides visual analytics for stakeholders that helps in decision-making.

Movement of a large crowd from one place to another smoothly is a challenging task. It becomes more critical where the large crowd from different nationalities move together in a defined time to perform specific duties such as Hajj. Hajj is one of the large congregations in the world, where more than 2 million pilgrims coming from all over the world gather in Makkah to perform the fifth pillar of Islam. To perform the Hajj rituals, the pilgrims should stay and move between the holy sites of Mashaer area1on specific days, and time. These spatio-temporal constraints are mandatory to follow to perform the Hajj acceptably. To manage the crowd efficiently, the authorities of Saudi Arabia grouped the pilgrims from all over the world in seven establishments based on nationalities. Specific places, routes and train stations are allocated to each establishment to perform spatio-temporal rituals in Mashaer area. In this paper, we present an optimised scheduling process that schedules the movement of pilgrims to perform spatio-temporal rituals safely using trains. The scheduling process depends on a variety of factors. Firstly, it depends on the train movement type, whether its B (Mina - Arafat), C (Arafat - Muzdalifah), or D (Muzdalifah - Mina). Secondly, the model also relies on the current pilgrims' camp location and the other camp location in the destination. Thirdly, the roads allowed from/to camps-stations also affect the scheduling process. Moreover, to avoid occurring crowd disasters, the scheduling process should monitor station capability to accommodate pilgrims. Our algorithm has been used in Hajj 2019 for three movements (B, C and D) in Mashaer Area. The result shows the analytics of our scheduling algorithm along with comparison between the scheduled time and actual time for each station in Mashaer area in Hajj 2019.

Monitoring and understanding traffic behaviour plays a vital role to handle the daily needs of the users in a smart way, especially in highly populated big cities. Events pulling in big crowds like religious gatherings or sports events, clashes between vehicular traffic movements are unavoidable which results in stoppages and hazardous conditions for the pilgrims, sometimes causes disorder and jam. The problem becomes more challenging when millions of users move from one place to another in a specific duration of time during Hajj. Hajj is one of the pillars of Islam where 3+ millions of pilgrims gather in Makkah, Saudi Arabia to perform their rituals. In this paper, we present an interactive platform to understand the movement of buses during Hajj using large scale GPS traces. We collected the GPS traces of more than 20,000 buses for 60 days for the movement of pilgrims to perform their Spatio-temporal rituals. An interactive platform is developed to store big spatial data and visualize the same. We analyzed the data and identified the routes, parking areas allocated to each establishment and commuting time of routes for different establishments. The knowledge extracted will be beneficial for stakeholders and decision-makers to enhance pilgrims mobility during the hajj by developing an intelligent transportation system such as scheduling, evacuation, optimizing the number of buses or roads for each establishment.

Large gatherings such as concerts, football matches, festivals need an efficient and effective tool to deal with various emergency scenarios. Such events require a proper plan to evacuate the affected areas smoothly and peacefully. The problem of evacuation becomes more complicated when we talk about Haj where 3+ million pilgrims from all over the world gather to perform rituals on specific date and time. In this work-in-progress paper, we present a “Capacity Aware Evacuation Planning using Multimodal Transportation (CAEPM)” platform, to evacuate pilgrims from Mashaer1area in case of disaster using multimodal transportation. The mode can be pedestrian, public/private buses and trains. The pilgrims from all over the world are grouped into seven establishments. Specific routes are allocated to each establishment to perform spatio-temporal rituals in Mashaer area. To manage the crowd and avoid stampede, internal roads are closed in Mashaer. To evacuate any area in Mashaer in case of any unfortunate event, it is very necessary to understand the allowed roads and mode of movements. In order to achieve the same, we received large scale GPS data of more than 20,000 buses from Haj 2018, train movement details, and camps assignments from different authorities of Kingdom of Saudi Arabia. We extracted knowledge from these collected data to identify the bus routes for movements during Haj for different establishments, number of buses assigned to each establishment, pick up points, drop off points, average speed, capacity of train stations, trains and roads using big data platform. We will use this knowledge to develop our novel CAEPM platform. Our proposed platform will provide innovative decision-analysis capabilities to various stakeholders at city and province levels supervising Haj and Umrah activities. Our proposed platform can be handy for pre-planning exercises, thus serving potentially to the control center for managing the Haj event.

In this article, we propose a threat management system (TMS) for Data-centric Internet-of-Things-based Collaborative Systems (DIoTCSs). In particular, we focus on tampering attacks that target shared databases and can affect the execution of the DIoTCS services. The novelty of the proposed system is to isolate the damage caused by tampering attacks into data partitions. We formulate the partitioning problem as a cost-driven optimization problem, prove its NP-hardness, and propose two polynomial-time heuristics. We evaluate a TMS experimentally and demonstrate that intelligent partitioning of the database improves the overall availability of the DIoTCS.

Analyzing and visualizing large datasets generated by real-time spatio-temporal activities (e.g. vehicle mobility or large crowd movement) are a very challenging task. Recursive delays both at middleware and front end applications limit the of usefulness of the real-time analysis. In this paper, we present a framework “Spatial-Crowd” that first handles spatial-temporal data acquisition and processing by scaling up the middleware components and its infrastructure. Then, it enables filtering, fixing, enriching and summarising the acquired dataset, readily available for client interfaces which usually are not scalable or built to manage such large datasets. This framework follows published subscriber model and allows users to subscribe to aggregated data streams instead of requesting data in real time. The framework is tested with data generated by a very large simulated dataset and performance showed a significant data reduction on the client side to enhance data visualisation.

The reliable delivery of data is important in designing Wireless Body Area Networks (WBANs) employed for critical applications such as e-health. In order to communicate the data reliably from the sensors to the base station, the data transmission technique (star or multi-hop) and the transmission power plays a very important role. As transmission power is increased, transmission distance is increased and the data can be sent reliably to far nodes. However, in WBANs, there is always a limit to increase the transmission power. Keeping the power level at some low threshold and increasing the distance between a sensor and the base station results in reduced received power which ultimately degrades the data transmission. Thus, for star data transmission technique, the point to ponder is the maximum separation between a sensor and the base station to transmit the data reliably. The reliability in WBANs can be analyzed through different parameters such as received power, received signal strength indicator, link quality indicator, packet error rate, packet reception rate, etc. This paper aims at performing a reliability analysis for WBAN through the mentioned parameters to suggest an optimal sensor/base station separation using star data transmission technique. This analysis is performed employing the default routing protocol in TinyOS called the Collection Tree Protocol. Our study considers different sensor placements on different parts of the body as well as different angular offsets between sensor and base station.

Mass gatherings and mass pedestrian flows such as during holy rituals are always challenging for the management as well as for the people attending them. The holy city of Makkah hosts millions of pilgrim coming from all over the world each year during Ramadan and Hajj seasons. One of the main ritual

Wireless Sensor Networks have been used extensively for environment data collection from dense as well as simple linear structure deployments. However, the need to collect data from all sensors in the network imposes constraints on internode distances. Critical issues related to optimal resource allocation still persist for even simplistic deployments like linear and hierarchical networks. In this work, we present a method for node placement in linear wireless sensor network with notion of provisioning reliable data delivery over extensive monitoring infrastructures. We provide a selection mechanism for required number of nodes that can cover a specific area while providing a margin for channel reliability. The reliability approach has been analyzed in a pipeline monitoring scenario and subsequently referred with analytical model to ensure consistency with minimum resource consumption and best network connectivity in real world. The proposed node placement strategy is based on a dynamic programming model for achieving reliability in received signals in a multi hop and long distance connectivity perspective.

In emergency health cases such as mass casualty incidents, the death ratio is still high due to lack of an automatic and intelligent system which timely observes and reports patient criticality. Indeed, the existing criticality assessment approaches are manual such as the established Simple Triage and Rapid Treatment (START). Accordingly, it is difficult for care givers to provide optimal healthcare, in particular, if the number of casualties outnumbers the responders. A challenge is how to automatically tag a possibly large number of victims with various types of disorders immediately after an incident and before the arrival of the paramedics. Such an automated tagging would provide for more optimized emergency response. We propose an automatic self-tagging methodology using body sensor networks that deliver relevant vital signs, i.e., respiratory rate, heart rate and mental status. We present three approaches to recognize and grade the criticality level of patients. The proposed approaches are generic and can be easily adapted to different scenario such as patients in intensive care units, patients in surgery and elderlies being monitored in their home. Being fully automated, our methodology is able to provide real-time tagging with higher accuracy and fine-granularity than the simplistic manual current systems. We demonstrate the viability of our self-tagging approaches by statistically demonstrating their accuracy compared to that of experts manual tagging.

With the increase number of wireless standards and the number of wireless enabled devices ranging from smart cell phones to notebooks, the new concept of Internet of Things where every tangible "Thing" will have an IP address and wireless connectivity is around the corner. In addition, there is a great interest of Cyber-Physical System interconnecting city's infrastructure together though wireless connections from bridges to water pipes to smart grid and intelligent transport systems. Not all of these devices will use the same wireless technology for communication and hence it will be very hard for them to exchange control and data messages. Moreover, different wireless technology standards are being used nowadays in many applications such as Bluetooth, WiFi, WiMAX etc. Each technology has a different stack of protocols, medium access control and encoding mechanisms dictating the way it works, operates and technical specifications. Each technology was proposed, developed and standardized to serve specific needs and applications. For example, Bluetooth standard was developed to interconnect small number of devices available within short distances from each other. WiFi was standardized to provide stations with wireless network connectivity through TCP/IP stack. Devices with same wireless technology can connect together to exchange control and data messages. With the increase number of wireless technologies available for users, one major difficulty is to interconnect different devices with different wireless technology together. For example, it is impossible for a notebook PC to connect to a Bluetooth device without having a Bluetooth dongle.

Wireless sensor networks (WSNs) are receiving more popularity in mission critical and delay sensitive industrial applications because they offer low latency and reliable message transmission. In applications like gas leakage detection, monitoring of pressure and industrial process control etc. reliable communication between sink and the sensing nodes is very important. In wireless sensor networks sensing nodes are placed very densely in different environments and mostly with no defined network topology. In industrial setup, the placement of the sensing nodes plays a very important role, most importantly it increases overall system throughput by efficiently transmitting the calibrated readings and providing maximum security to the industrial devices. In this paper we aim to investigate, how different topological settings effects packet delivery ratio (PDR) and End-to-End delay in wireless sensor networks? This paper also focuses on the performance study of three different network topological settings for mission critical applications. We evaluated the performance of wireless sensor network (WSN) by placing the sensor nodes in three different topological designs namely Linear, Tier one and Split Tier one.

Analytical models for IEEE 802.11 Distributed Coordination Function (DCF) play important roles in performance estimation, protocol optimization and admission control for wireless networks utilizing this MAC protocol. While a myriad of models for IEEE 802.11 DCF channel access delay and throughput exist, such models' accuracy is generally limited to rather narrow ranges of scenario parameters. Our investigations single out the inaccuracy in modeling the backoff process as the primary reason for the said deviations. In this paper, we introduce two highly accurate models for IEEE 802.11 DCF protocol in a single-hop setting under both saturated and unsaturated traffic loads. First, a crucial augmentation to the classical model proposed by Bianchi is presented for the saturation load analysis to account for channel state during the backoff countdown process, resulting in a highly accurate estimation of collision probability, channel throughput and channel access delay. We then extend this accurate model to unsaturated traffic cases through an iterative approach which similarly results in highly accurate performance metric estimations for a wide range of parameters. Both models have been evaluated through simulations and in comparison with existing analytical models.

Directional antennas offer many potential advantages for wireless networks such as increased network capacity, extended transmission range and reduced energy consumption. Exploiting these advantages, however, requires new protocols and mechanisms at various communication layers to intelligently control the directional antenna system. With directional antennas, many trivial mechanisms, such as neighbor discovery, become more challenging since communicating parties must agree on where and when to point their directional beams to enable communication. In this paper, we propose a fully directional neighbor discovery protocol called Sectored-Antenna Neighbor Discovery (SAND) protocol. SAND is designed for sectored-antennas, a low-cost and simple realization of directional antennas, that utilize multiple limited beamwidth antennas. Unlike many proposed directional neighbor discovery protocols, SAND depends neither on omnidirectional antennas nor on time synchronization. In addition, SAND performs neighbor discovery in a serialized fashion allowing individual nodes to discover all potential neighbors within a predetermined time. Moreover, SAND guarantees the discovery of the best sector combination on both communication ends allowing more robust and higher reliability links. Finally, SAND gathers the neighborhood information in a centralized location, if needed, to be used by centralized networking protocols. The effectiveness of SAND has been assessed via simulation studies and real hardware implementation.

Wireless sensor networks have been used to gather data and information in many diverse application settings. The capacity of such networks remains a fundamental obstacle toward the adaptation of sensor network systems for advanced applications that require higher data rates and throughput. In this paper, we explore potential benefits of integrating directional antennas into wireless sensor networks. While the usage of directional antennas has been investigated in the past for ad hoc networks, their usage in sensor networks bring both opportunities as well as challenges. In this paper, Sectored-Antenna Medium Access Control (SAMAC), an integrated cross-layer protocol that provides the communication mechanisms for sensor network to fully utilize sectored antennas, is introduced. Simulation studies show that SAMAC delivers high energy efficiency and predictable delay performance with graceful degradation in performance with increased load.

In this paper, we present a novel packet delivery mechanism called Multi-Path and Multi-SPEED Routing Protocol (MMSPEED) for probabilistic QoS guarantee in wireless sensor networks. The QoS provisioning is performed in two quality domains, namely, timeliness and reliability. Multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options. In the reliability domain, various reliability requirements are supported by probabilistic multipath forwarding. These mechanisms for QoS provisioning are realized in a localized way without global network information by employing localized geographic packet forwarding augmented with dynamic compensation, which compensates for local decision inaccuracies as a packet travels towards its destination. This way, MMSPEED can guarantee end-to-end requirements in a localized way, which is desirable for scalability and adaptability to large scale dynamic sensor networks. Simulation results show that MMSPEED provides QoS differentiation in both reliability and timeliness domains and, as a result, significantly improves the effective capacity of a sensor network in terms of number of flows that meet both reliability and timeliness requirements up to 50 percent (12 flows versus 18 flows).

In this paper, we present a novel packet delivery mechanism called multi-path and multi-speed routing protocol (MMSPEED) for probabilistic QoS guarantee in wireless sensor networks. The QoS provisioning is performed in two quality domains, namely, timeliness and reliability. Multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options. In the reliability domain, various reliability requirements are supported by probabilistic multipath forwarding. All these for QoS provisioning are realized in a localized way without global network information by employing localized geographic packet forwarding augmented with dynamic compensation, which compensates the local decision inaccuracy as a packet travels towards its destination. This way, MMSPEED can guarantee end-to-end requirements in a localized way, which is desirable for scalability and adaptability to large scale dynamic sensor networks. Simulation results show that MMSPEED provides QoS differentiation in both reliability and timeliness domains and, as a result, significantly improves the effective capacity of a sensor network in terms of number of flows that meet both reliability and timeliness requirements.