Abstract – Vehicular Ad-Hoc Networks have become an emergent research topic as Vehicle-to-Vehicle Communications (V2V) offer some unique advantages. These advantages include a slow/stopped vehicle advisor capability (which advises the driver when any vehicle ahead is stopped or traveling slower than 20 mph), an emergency electronic brake light (which notifies the driver when a vehicle ahead is suddenly braking hard), a lane change and blind spot advisor, an intersection collision warning, and a forward collision avoidance capability with automatic braking. The intent is to use these emergent commercial applications for the military and to improve the communication between military vehicles in various tactical situations. This paper adds to this area of study with some new practical findings pertaining to vehicular networks, specifically convoy communications and unmanned vehicle control. Besides considering of principles and rules of military convoys, experiments and simulations with military scenarios are necessary to improve vehicular networking for the military use. Global Positioning Systems (GPS) tracing has become a key point in this field as we look at GPS traces and their effects on throughput improvement and reliability on vehicular communications. These issues were analyzed through experiments using the ORBIT testbed.

Index Terms—communications, vehicular ad-hoc networks, military convoys, situation awareness

I. INTRODUCTION UTOMOBILE accidents account for approximately 40,000 fatalities in the US. In addition, accidents are a primary contributor to traffic congestion, which results in

approximately 5.7 billion hours of wasted travel time annually in the US. In a fashion similar to how mobile phones exploit their surroundings by finding optimal signals, vehicles should be able to find signals from other vehicles and get information.

Networking technology has many commercial uses and is already being considered in commercial settings. US automaker General Motors has done extensive work modeling traffic conditions [1]. In addition, several automakers are jointly working on Vehicle-to-Vehicle Communications (V2V) using the 5.9 GHz frequency to reduce blind spot collisions and “tailgating” crashes [2]. These commercial applications have been used in a fairly static environment with known roadways where situations change relatively slowly.

This work was conducted under the Department of Defense Engineer and

Scientist Exchange Program.

In contrast to commercial use, the tactical environment of military operations is more complicated due to the highly unpredictable nature of traveling in a war zone. The military operates under the threat of inclement weather conditions, nuclear-biological-chemical (NBC) hazards, Improvised Explosive Devices (IEDs), snipers or other asymmetric forces. In addition, the majority of military operations occur during the night.

One common form of transportation is the military convoy. A convoy consists of anywhere from 3 - 100 military vehicles that proceed in a fixed order during a mission. The military vehicles include personnel carriers, supply carriers, tanks, ammunition suppliers, fuel suppliers, protected vehicles and special operations vehicles. Convoys can benefit by networking the member vehicles to share their information. Networking of these vehicles will provide immediate transfer of real-time information among them in order to help each convoy member to realize potential hazards such as potential and actual technical breakdowns, or localized attacks. In addition to the conventional traffic and vehicle maintenance data (accidents, damage and break downs), incident and position information (e.g., battle field information, enemy threats, blockades, mines, passed checkpoints) of the other convoy vehicles will be transferred.

Another unique military application is unmanned vehicle control, such as having multiple vehicles being driven by one driver or vehicles controlled robotically, to minimize the potential for human casualty. Currently, military convoy drivers operate vehicles for long periods of time with little or no rest. A practical application to this scenario is to send “dummy” vehicles ahead of the mission to help identify IEDs, land mines and other road hazards. Using this approach, distinct vehicles will foray into the field looking for specific types of obstructions.

The Army is doing similar research in this field to better equip the soldier in the battlefield. The Army’s Tank-Automotive Research, Development and Engineering Center (TARDEC) is working on convoy automation to make convoy operations simpler. Vehicle automation of the driving function allows soldiers to rest, run diagnostics on their vehicle or other vehicles in the convoy, or perform critical duties as necessary. In addition, the Army’s Future Combat Systems (FCS) is working on an Autonomous Navigation System (ANS) for robotic piloting of ground vehicles.

Wireless, Mesh & Ad Hoc Networks Military Convoy Location and Situation Awareness

Martin Preuss, Shery Thomas, US Army Communications-Electronics Research Development and Engineering Center (CERDEC), Fort Monmouth, NJ, {martin.preuss, shery.thomas}@us.army.mil

A

II. PROBLEM STATEMENT Computers have become ubiquitous and a necessity in our lives and their uses are ever increasing. In regards to vehicular network applications, all drivers participating in a convoy act differently. Because of potential for miscalculations and to adjust for erratic driving [3], readings from multiple cars have to be taken into account. There is also the need to develop location awareness protocols and network architecture to account for the geographical (3-D) environment. An unknown environment with different types of terrain, climate, building development or visibility conditions (like mountainous terrain, desert climate, urban areas or darkness) has to be considered in a realistic model. This is necessitated by the different scenarios in which vehicles engage. They include high node density, the requirement of higher bandwidth, low antenna heights, channel fading, higher frequency rates, network loading, and radio interference, to name just a few. In the military setting, we also look at location privacy, temperature variance, and other environmental factors.

Figure 1 shows the infrastructure upgrades that have to be

in place for this technology to be fully deployable. A vehicle must have in place an on-board unit, sensors, and a GPS. In addition, the roadways should have roadside base stations and various sensors to make the system fully functional for commercial use.

Figure 2 shows the ideal situation of this technology.

There are heavy infrastructure requirements for both roadways and vehicles. As this is the case, military applications would consider only parts of this technology due the dynamic nature of the military operations. It will not be practically feasible to setup roadways in this way. Military vehicles have to be equipped with only on board units that enable a self-sustaining communication within a convoy without roadway

infrastructure. Therefore, there is only inter-vehicle communication involved (see Fig. 3).

Compared with the commercial environment, the military environment is unknown to the operating soldiers and dynamic. There is no roadside infrastructure available and mobility must be exploited. Security has to be tightened to form a low probability of detection. Therefore, communication must be minimized considerably. All operations must take place in a covert manner.

Priority standards are based on the chain of command. All the while, speed and quickness is the main issue. A system is not practical if it takes over a few seconds to transmit and receive information in mission critical situations. Validating traffic information without imposing a significant communications overhead is a difficult problem to overcome. In addition to the promises and benefits to society like the reduced chance of fatality and less time stuck in traffic, vehicular ad-hoc networks present opportunities for those that want to cause harm. If enemy forces were to have access to the system, there may be adverse effects to our own military forces. Antenna placement is another issue that is to be considered due to the vehicular settings. The placement of antennae on a vehicle affects throughput, received signal strength, and packet error rate [4]. There are many concerns in terms of interference, as multiple radios may coincide. Further challenges are to handle like to optimize hopping speeds, rapid dynamic detection of nearby nodes, system and protocol support, range optimization and safety and security issues.

III. APPROACH The project using vehicular networking for military convoys contains several phases: The basic part with the studying the general principles of vehicular networking, the adaptation of the military requirements, the testing in the ORBIT indoor testbed, the procurement and modification of hardware and software and the testing as outdoor field experiment. Related to vehicular networking for this study we analyzed GPS characteristics, created simplified military scenarios and started testing in the two-node network ORBIT. The GPS device was used for location awareness and communication with the node. Initially, we looked at a GPS device in order to get the location information of the vehicles. Currently manufactured GPS devices take readings at a rate of one reading per second. Vehicle speed is approximately 17 meters per second leading to an error rate between 0 – 30 meters for GPS. A GPS device taking readings at 5 readings per second was studied. The discrepancy in the location was reduced due to the multiple readings. With current GPS systems, many packets of voice/data may be lost because of an inaccurate estimate of a vehicle's position. With more frequent readings, this could be prevented [5]. For the first experiments we used the tool “IPtables” found through the Linux Kernel. It enables to facilitate with building firewalls (to prevent outside forces access to the system) and allowing for packet dropping. Through this, packets were varied and their corresponding characteristics

Figure 1: Infrastructure Design

Figure 2: Commercial Vehicle to Vehicle Communication

were analyzed. We also used the “IPerf Traffic Generator” supplied through the University of Illinois-Urbana Champaign. To focus on the military application we created a convoy model with different scenarios. Military convoys are defined as a group of vehicles organized under a single commander for movement. They follow certain principles which differ from usual traffic rules and consist of the march column, the serial and the march unit. In a convoy the responsibility lies with the convoy commander who is in radio contact with the lead, the rear vehicles and his superior command. The lead vehicle is the pace setter/navigator, which ensures proper route and checks for changes to orders at predetermined points. There is a trail element, which contains a trail officer/non commissioned officer responsible for maintenance and medical support of the convoy. Not every vehicle gets all information but certain information, like NBC threats, should be transferred directly to each vehicle. Within the different communication circles, vehicle drivers are only provided with the information from their March unit. If a convoy consists of 5 groups (companies) with a distance of 500m between them only each group of vehicle uses the same radio frequency. Convoy operations are characterized by intensive planning and the decisions of the convoy commander/march unit commander of the mission, based on the situation information he receives. If unexpected incidents arise, it is especially difficult for the convoy commander to make well informed decision, due to the limited amount and nature of the information he receives and the short time frame in which he has to act. To provide more information more quickly, vehicular networks could simplify decisions or even take over certain decisions automatically during a military convoy operation. The differences encountered in military operations, as compared with ordinary traffic situations, consist of the type of information to be sent and received (voice, video data), the traffic circuits and the security aspects. Convoy communications are normally via radio [6]. Signals must be planned, rehearsed and understood by all the personnel involved in the movement. Radio allows rapid transmission of orders and messages between widely separated vehicles but when radio contact is not possible (e.g. poor reception, security risk) or turned of visual communications are used. Most of the information is provided via voice to keep the convoy personnel focused on vehicle operation and the surrounding environment. Different voice traffic channels are employed to communicate with a higher command or within the March unit. Security aspects are an important factor to protect our troops. This includes a 360 degree perimeter of security, transmission reliability and security providing a low probability of intercept. In the future, there will be greater exploitation of artificial intelligence. Decisions will be made instantaneously and human intervention will be minimized. Visual data from sensors or drivers of other vehicles will be processed and transmitted to illustrate road hazards.

With this background information we created two scenarios which were simplified for the testing in the simulation and the field experiment. The situation considered was the movement to another support post. The scenarios differ in various vehicle intervals, convoy speeds and the number of vehicles. The first scenario (platoon movement) is based on an urban area and a 5 miles distance with 5 vehicles (vehicle intervals: 50m, speed: 15 MPH). The second scenario (company movement) is designed for a rural area and a 10 miles distance with 10 vehicles (vehicles intervals: 150m, speed: 25 MPH). Vehicular networking in military convoys can help to adjust vehicle speeds (accident prevention), notify drivers of enemy detection, coordinate the response of emergency personnel, and transfer situation information to the commander or maintenance data about the vehicle to the trail element. The overall goal for military communications is to improve the network topology and to reach the complete battlefield network within the Network-Centric Warfare (NCW). Convoy communications using vehicular networking could improve communications between vehicles and help to get the Common Relevant Operational Picture (CROP) to all.

IV. SUMMARY OF RESULTS The Open-Access Research Test-bed for Next-Generation

Wireless Networks (ORBIT) test bed was used for our experiments. ORBIT consists of several test grids with 2 to 400 nodes. The main test grid consists of 400 wireless nodes each transmitting in various desired frequencies with multiple radios. Each node consists of 2 processors with 802.11a/b/g and Bluetooth capabilities to model mobile ad-hoc networks. For the

Figure 3: Convoy Communication

first experiments we only used the two node test grid to simulate the data flow from one static vehicle to another. Mobility like in the different scenarios will be regarded in future experiments using the main ORBIT testbed.

Several routing protocols currently exist for vehicular settings. Greedy Perimeter Stateless Routing (GPSR) uses the positions of routers and packet's destinations to make packet forwarding decisions [7]. GPSR makes forwarding decisions using only information about a router's immediate neighbors in the network topology. This protocol allows for a more mobile routing environment compared to the standard ones (AODV, OLSR, etc.). It was found that a 50% random packet drop reduces throughput by 50 - 55% from ten independent tests. This can be seen from figure 4 below. It shows that if the packets are dropped at a rate of 50% from source to destination, the receiver will gain on average only about half of the information from the sender. As the drop rate is increased, more and more data will be lost to the point where it cannot be understood properly. The data gathered is fairly consistent. The conclusion is that data throughput between nodes is proportionately related to the packet drop. In this scenario, a file will take roughly twice as much time to be received compared with no packets being lost.

To begin, a random packet drop rule was implemented. From this, the two-node network ORBIT testbed was used to implement this same rule. One was specified as the static sender node and the other as the receiver node. The throughput drop from one node to the other was measured through use of the “IPerf Traffic generator.” Utility software that creates a generic table with probabilistic modeling calculations using the Gaussian model was used for generating particular drop rates per source. The testbed was populated based on wireless neighbors and their signal strength. The dynamic nature of roadways in which no two scenarios are ever the same seems to be a major hurdle to overcome. This has to be resolved only through rigorous testing techniques. A multi-hop was created to emulate a real-world scenario of several vehicles. The above shows the directed graph of the pattern from the source sender node to the sink receiver node. In this experiment, four nodes are randomly chosen based on the ones that are active. Each one is assigned the rate and percentage of those accepted. The real world scenario can only be seen through testing in outdoor environments and reciprocating those characteristics in the ORBIT test bed.

Mobility was emulated used MAC filtering, which controls the topology of a network without moving nodes. To emulate moving nodes, the system will select sending and receiving radios from the grid that best represent the positions of the moving nodes as given by a mobility scenario. To emulate large-scale networks the system can raise the noise floor in the environment, which emulates a greater distance between transmitter and receiver. The main concern in vehicle to vehicle infrastructure is the lack of repeatability as no two situations are ever the same. The only resolution is through brute force experimentation. Another emulation method is through a software spatial switching approach. It emulates mobility by switching a moving node to different radio and antenna positions as time progresses.

V. FUTURE WORK This project was started in the fall of 2007. We are currently in the process of implementing the ORBIT indoor testbed using multiple vehicle models through noise injections to find optimum sampling rates and results. Gupta and Kumar [9] showed that the throughput per source-destination pair is Θ(1/√(n log n)). Therefore, this result can be used as our basis for further work. Our analysis of this work will involve further studies as source and destination are increased and interchanged. It will be further analyzed in outdoor, real-world vehicle-to-vehicle experiments to find discrepancies and differences from the ideal scenario. From these findings, more accurate models can be realized with use of mobility.

Figure 4: Throughput Analysis

Figure 5: Multi-Hop Path

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http://media.gm.com/us/gm/en/technology/advanced_technology/safety_telematics/V2V_communications.htm

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